DTU ProjectLab - User contributions [en-gb]

Monte Carlo Simulation of Risk

2014-12-02T14:43:01Z

Tallimac: /* Risk */

== Abstract ==

”To dare is to lose one’s footing momentarily. To not dare is to lose oneself” - Søren Kierkegaard

Staying dynamic is a means of survival in today’s modern society, thus the expression the burning platform. In order for companies to stay competitive and sustain their businesses, they have to either become cost leaders or product leaders i.e. innovative. In order to realize this, companies have to dive into new areas of business, explore new possibilities in terms of evolving their products and/or processes. During the process of exploring and maintaining dynamism, uncertainty is inevitable due to the respective volatile nature of the markets within which the diverse companies operate in. Companies can’t foresee the future, hence don’t know how their respective markets will react to their new ways of doing business, new products or on the other hand if they are resilient enough to sustain unexpected drawbacks when exploring new paths. Thus it can be concluded that risk is incorporated in the DNA of any project, program or portfolio management, therefore “Risk Management” is a necessity for companies to continuously embark when exploring new dimensions in order to mitigate risk and suppress their corresponding consequences.
There are several ways in which risk assessments can be conducted. This article provides a profound description of how to conduct a quantitative risk assessment by means of utilizing Monte Carlo simulations.

[[Category:''Project, Quantitative risk analysis, Monte Carlo Simulation'']]

== Risk ==
[[File:pure&speculative risk.png|400px|thumb|right|Figure 1: Categorize of risk<ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref>.
]].
It is basically inevitable not to associate risk and uncertainty to any human activity<ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> although they can vary from activity to activity, depending on what’s at stake. In literature, risk in management has been defined as the probability (chance) of an event occurring, which could eventually result (uncertainty) into a negative impact (consequence) on a particular project in context<ref name=''WANG & HUANG''> ''WANG, Xing-xia; HUANG, Jian-wen,Risk analysis of construction schedule based on Monte Carlo simulation,International Symposium on Computer Network and Multimedia Technology (CNMT 2009)'' </ref><ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> thus risk is an expected loss over time.

'''Risk = Probability of risk occurring ×Impact of risk occurring'''

Risk can be categorized as either pure or speculative <ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> <ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref> , as depicted in Figure 1 Regarding pure risk, there is no benefit or gain pertained to it, thus loss is the only possible outcome e.g. companies exposed to fraud or damage of assets etc. On the other hand, speculative risk can result into an uncertain degree of loss or gain e.g. a company can either gain or lose on investing in a new product, since there is a risk of market rejection. In this article emphasis is laid on speculative risk.

 
 
 

== Risk Management ==
When dealing with project, program or portfolio management it is crucial to understand that a lot of uncertainties (risks) are pertained to them, since their eventual benefits are projected into the future (vision/goal). These uncertainties occur randomly within the lifecycle of the different categorize of management, thus eventually resulting into delays, budget overruns and eventually terminations, if not managed hence the importance of risk management.

According to ISO 31000 <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management is the coordination of activities to direct and control an organization with regards to risk. In literature<ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management can be decomposed into four continuous phases i.e. risk - Identification, Analysis, Evaluation and Monitoring as depicted in Figure 2 and elaborated below. Before embarking on a risk management course, it is crucial to commence with a risk management plan. Risk management planning is the structuring and detailing of how the risk management process is going to be conducted throughout the lifecycle of a project <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. Subjects such as methodology, practices, roles and responsibilities, sequence and timing of activities are pertained to risk management planning <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>.
[[File:phases of risk management.png|400px|thumb|center|Figure2:Phases of risk management
]].

*'''Phase 1: Risk Identification ''':
This phase involves the pinpointing of potential risks that might eventually affect a particular project in context and their causes. It is usually conducted by domain experts by means of brain storming<ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>

*'''Phase 2: Risk Analysis''':
This phase involves the comprehension of the nature of the initially identified risks and an estimation of their consequences <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>, since risk have different severity degrees. Risk analysis can be sub divided into qualitative and quantitative risk analysis. Both approaches can be utilized in one project but in order to conduct a quantitative analysis, estimates from the qualitative analysis is needed. On the other hand risk analysts can chose to stop with a qualitative risk analysis if the project is not too big.

'''Qualitative risk analysis''':In a qualitative risk analysis descriptions and estimates (mostly in monetary terms) of the different consequences pertained to the different risks, estimates of their occurrence (frequency) and means by which they can be mitigated are listed. Furthermore the different risks are prioritized, which provides the foundation for the evaluation phase. Figure 3 shows an overview of how the different risks can be prioritized according to severity.

[[File:Risk matrix.png|400px|thumb|center|Figure 3:Risk Matrix for prioritizing risks bases on frequency and consequences in monetary terms
]].

'''Quantitative risk analysis''':Quantitative risk analysis is a process of quantifying various impacts of identified risks imposed on a specific project in context, by means of using their estimates from the qualitative analysis. The quantification process is conducted by allocating different probability distributions to respective risks and thus simulating hypothetical events (scenarios) of the identified risks<ref name=''Suhobokov''> ''Suhobokov, Alexander; Application of Monte Carlo Simulation Methods in Risk Management,Journal of Business Economics and Management 2007'' </ref>

*'''Phase 3: Evaluation''':
This phase involves the evaluation of the different risks by means of comparison in terms of severities and frequencies thus the establishment of tolerability levels. Organizations can in this phase chose to accept, try to avoid, transfer the risks or terminate the project if the risks are too high.

*'''Phase 4:Risk Monitoring and Control ''':
This phase involves the continuous monitoring, supervising and controlling of the different identified risks <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> . New risks that were not initially identified could also surface along a projects lifecycle, thus the necessity of continuously reassessing and revaluating risks. Figure 4 depicts a more detailed process of Risk management.
[[File:Risk analysis.png|400px|thumb|center|Figure 4: Different processes pertained in the four phases of risk management<ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>
]].

== Monte Carlo Simulation ==
In order to elaborate on what a Monte Carlo simulation is, the following terms must be understood in order to facilitate the comprehension of the concept of Monte Carlo simulation:

*'''Random variable (Stochastic variable)''':
A variable is any defined characteristic that is subjected to variation either due to natural and /or imposed factors e.g. height, age, temperature etc. A random variable on the other hand is any function that allocates a numerical value to each possible outcome<ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> i.e. a measurement or a count of a variable (characteristic) that varies randomly according to a certain pattern. Random variables can be categorized into two categories depending on the type of outcome from a certain pattern; Discrete or continuous. Discrete random variables are outcomes of random variables that are either finite or countably infinite, whereas if the set of possible outcomes of a random variable is an interval, then it is continuous <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> <ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> . E.g. when throwing two deices fifty times simultaneously as depicted in Figure 5, a player can’t predict what the outcome will be due to uncertainty, therefore when the outcome results into 5 and 6, the values are termed as random variables.

[[File:probability distribution.png|400px|thumb|center|Figure 5: Frequency table of dice throwing
]].

*'''Probability distribution''':
Probability is the chance of something in particular occurring e.g. when playing Russian roulette with a six chamber revolver loaded with one bullet, the probability of pulling the trigger thus igniting the bullet is P(X=bullet)=1/6. A distribution on the other hand is the listing of viable/intervals of values of a characteristic (variable). A probability distribution can thus be defined as the probability structure of a random variable <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> i.e. in other words the probability (chance) of a random variable taking on a value within a certain set of possible outcomes, whereas each of those outcomes have a certain probability of occurring. It should be noted that when dealing with discrete random variables, the probability distribution of the variable is referred to as the probability mass function, and on the other hand probability density function when dealing with continuous random variables <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> .

Thus a Monte Carlo simulation can be defined as a quantitative approach of quantifying risks, by means of utilizing a probability distribution <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. This is because risk is random and uncertain; therefore it can be classified as a random variable i.e. something that occurs by chance. Hence a Monte Carlo simulation of a particular project in context selects random variables from a given probability distribution of risk that is modeled by means of estimates. This is in order to figure out which risk has the highest certainty of occurring based on the probability distribution allocated to it. Furthermore, a Monte Carlo simulation enables the correlation of various risk factors, by means of incorporating a probability distribution for each conditional relationship<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref><ref name=''Hargreaves''> ''Hargreaves, John,Quantitative Risk Assessment in ERM, Enterprise Risk Management 2011'' </ref>. This thus facilitates the process of comprehending and assessing the various impacts of risks associated to a certain project and the pinpointing of ways in which they relate to each other.

== Methodology ==
A probability distribution model of identified risks are attained by means of acquiring historic or domain expert data (elaborated below) and thus conducting a data fitting, to observe which distribution model suits the various identified risks the most. A Monte Carlo simulation utilizes the probability distribution by means of selecting random variables (represents various risks) within a defined range of parameters. The process of selecting random variables is performed with multiple iterations to simulate different possible outcomes in order to find the risk with the highest certainty of occurring.
For the sake of simplifying and exemplifying; think of a company that is about to initiate a new project, the project group conducts a risk analysis thus identifying six risks that could affect their project during the qualitative risk analysis. The identified risks along with their estimates are then entered into a Monti Carlo simulation which then processes the data provided. An analogy in this case with regards to the Monti Carlo simulation is continuously throwing a six sided dice marked with R1, R2….. R6 (represents the identified risks) e.g. up to a thousand times and thus recording the frequencies at which they occur. All the six identified risk have an equal chance of occurring in this scenario i.e. 1/6 thus making the distribution to be applied, a uniform distribution, which is elaborated in the Data fitting section. This is a good way of understanding how a Monti Carlo simulation functions since it selects random variables from a given distribution and records the frequencies at which they occur. This iteration is done a couple of times to find the most likely risks. In order to conduct a Monti Carlo simulation of a given project, the following phases need to be undertaken:

=== Data source ===
It is crucial to attain applicable data when working with a Monte Carlo simulation. This is in order to grasp vital information embedded in the final results. The more precise the data inputs are, the more valuable data can be extracted. In other words trash in equals to trash out.
When dealing with data sourcing for a Monte Carlo simulation regarding risk, there are two forms of data that could be applied i.e. historic data (available) or domain expert knowledge (input from qualified experts)<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref>. A very good ex. of where domain expert knowledge is applied when conducting a risk analysis is within the construction industry. Experts that have worked within the field for years, thus acquiring knowledge and experience, use their foundation as a source for establishing estimates of price and time pertained to a certain building project. Historic data on the other hand can be sales data, cost of similar projects from the past or data from historic events etc.

=== Data fitting ===
Data fitting is the process of identifying the most appropriate probability distribution to simulate observed (historic) or defined (domain expert) data. E.g. by plotting a histogram over observed data and then plotting a distribution over it (normal distribution) as depicted in Figure 4 1 to visually inspect if the distribution fits. In many cases it can be very difficult to pinpoint the exact distribution to apply, when there is more than one distribution that can fit. Thus the necessity of goodness of fit, whereby the Chi-squared and Kolmogorov-Smirnoff test can be applied to test how good a distribution fits to a set of observed data (Gupta, 2013)
Common distributions applied in the simulation of risks in a Monti Carlo simulation are elaborated as follows:

*'''Normal Distribution''':
When utilizing the normal distribution two parameters must be defined, i.e. the mean (average of the data set) and the standard deviation (the difference between the various random variables from the mean). It is an unbounded distribution i.e. the possible outcomes of the random variables covers all possible values. The values centered about the middle (mean) of the distribution are most likely to occur with a probability of 68 % i.e. with one standard deviation from the mean. This can be seen in Figure 6, where the random variables around the mean have the highest frequencies. The normal distribution can be utilized to simulate risk of inflation by means of historic data thus mitigating the risk of budget overruns.

[[File:Normal distribution.png|400px|thumb|center|Figure 6: Illustration of data fitting and a normal distribution
]].

*'''Uniform Distribution''':
In this form of distribution all variables have an equal chance of occurring as initially mentioned in the dice analogy example of a Monte Carlo simulation. Two parameters have to be defined when utilizing the uniform distribution i.e. the minimum and maximum values as depicted in Figure 7.

[[File:Uniform distribution.png|400px|thumb|center|Figure 7: A uniform distribution, a =minimum and b = maximum
]].

*'''Triangle Distribution''':
This form of distribution is mostly applicable when dealing with domain expert data. When applying the triangle distribution three parameters have to be defined i.e. the minimum (optimistic), most likely (mode) and maximum (pessimistic) values<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref> as depicted in Figure 8. Triangle distributions are also bounded distributions since the possible outcomes of the random variables can only range within a defined interval

[[File:Triangle distribution.png|400px|thumb|center|Figure 8: Triangle distribution, a = minimum, b = mode and c = Maximum
]].

=== Iteration process ===
In this phase a Monte Carlo simulation software e.g. @Risk (works with excel), Latin hypercube, MATLAB etc. conducts a selecting process of random variables and identifies the most likely risks to occur based on the initially chosen probability distribution. Thereby simulating the likelihood of forecasted risks based on provided estimates.

== Conclusion ==

Monte Carlo Simulations are utilized in facilitating the process of identifying the most likely risks to occur during a project which could obstruct progress, by means of quantifying them. This thus facilitates the process of understanding and evaluating risks. Monte Carlo simulations are particularly fruitful when dealing with large projects since different risks can be modeled and a distribution can also be used in defining how the different risks relate to each other thus making it more realistic. The advantages and disadvantages pertained to Monti Carlo Simulations are as follows:

'''Advantages'''
* Reduced cost, due to the enablement of quantifying and mitigation risk prior to the implementation of its respective project.
* Acquired results are probabilistic thus apart from showing what eventually could happen, it also shows the likelihood.
* Easier to estimate intervals than to a specific value.

'''Disadvantage'''
* A certain degree of uncertainty on forecasted models, due to assumptions of the future. i.e. projection into the future, where there is no data availability therefore having to settle with estimations
* If the probability distribution is not suitable for a particular risk simulation, the output will not be useful i.e. garbage in garbage out.

== References ==

<references />

Monte Carlo Simulation of Risk

2014-12-02T14:42:52Z

Tallimac: /* Risk */

== Abstract ==

”To dare is to lose one’s footing momentarily. To not dare is to lose oneself” - Søren Kierkegaard

Staying dynamic is a means of survival in today’s modern society, thus the expression the burning platform. In order for companies to stay competitive and sustain their businesses, they have to either become cost leaders or product leaders i.e. innovative. In order to realize this, companies have to dive into new areas of business, explore new possibilities in terms of evolving their products and/or processes. During the process of exploring and maintaining dynamism, uncertainty is inevitable due to the respective volatile nature of the markets within which the diverse companies operate in. Companies can’t foresee the future, hence don’t know how their respective markets will react to their new ways of doing business, new products or on the other hand if they are resilient enough to sustain unexpected drawbacks when exploring new paths. Thus it can be concluded that risk is incorporated in the DNA of any project, program or portfolio management, therefore “Risk Management” is a necessity for companies to continuously embark when exploring new dimensions in order to mitigate risk and suppress their corresponding consequences.
There are several ways in which risk assessments can be conducted. This article provides a profound description of how to conduct a quantitative risk assessment by means of utilizing Monte Carlo simulations.

[[Category:''Project, Quantitative risk analysis, Monte Carlo Simulation'']]

== Risk ==
[[File:pure&speculative risk.png|400px|thumb|right|Figure 1: Categorize of risk<ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref>.
]].
It is basically inevitable not to associate risk and uncertainty to any human activity<ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> although they can vary from activity to activity, depending on what’s at stake. In literature, risk in management has been defined as the probability (chance) of an event occurring, which could eventually result (uncertainty) into a negative impact (consequence) on a particular project in context<ref name=''WANG & HUANG''> ''WANG, Xing-xia; HUANG, Jian-wen,Risk analysis of construction schedule based on Monte Carlo simulation,International Symposium on Computer Network and Multimedia Technology (CNMT 2009)'' </ref><ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> thus risk is an expected loss over time.

'''Risk = Probability of risk occurring ×Impact of risk occurring'''

Risk can be categorized as either pure or speculative <ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> <ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref> , as depicted in Figure 1 Regarding pure risk, there is no benefit or gain pertained to it, thus loss is the only possible outcome e.g. companies exposed to fraud or damage of assets etc. On the other hand, speculative risk can result into an uncertain degree of loss or gain e.g. a company can either gain or lose on investing in a new product, since there is a risk of market rejection. In this article emphasis is laid on speculative risk.

 
 

== Risk Management ==
When dealing with project, program or portfolio management it is crucial to understand that a lot of uncertainties (risks) are pertained to them, since their eventual benefits are projected into the future (vision/goal). These uncertainties occur randomly within the lifecycle of the different categorize of management, thus eventually resulting into delays, budget overruns and eventually terminations, if not managed hence the importance of risk management.

According to ISO 31000 <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management is the coordination of activities to direct and control an organization with regards to risk. In literature<ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management can be decomposed into four continuous phases i.e. risk - Identification, Analysis, Evaluation and Monitoring as depicted in Figure 2 and elaborated below. Before embarking on a risk management course, it is crucial to commence with a risk management plan. Risk management planning is the structuring and detailing of how the risk management process is going to be conducted throughout the lifecycle of a project <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. Subjects such as methodology, practices, roles and responsibilities, sequence and timing of activities are pertained to risk management planning <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>.
[[File:phases of risk management.png|400px|thumb|center|Figure2:Phases of risk management
]].

*'''Phase 1: Risk Identification ''':
This phase involves the pinpointing of potential risks that might eventually affect a particular project in context and their causes. It is usually conducted by domain experts by means of brain storming<ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>

*'''Phase 2: Risk Analysis''':
This phase involves the comprehension of the nature of the initially identified risks and an estimation of their consequences <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>, since risk have different severity degrees. Risk analysis can be sub divided into qualitative and quantitative risk analysis. Both approaches can be utilized in one project but in order to conduct a quantitative analysis, estimates from the qualitative analysis is needed. On the other hand risk analysts can chose to stop with a qualitative risk analysis if the project is not too big.

'''Qualitative risk analysis''':In a qualitative risk analysis descriptions and estimates (mostly in monetary terms) of the different consequences pertained to the different risks, estimates of their occurrence (frequency) and means by which they can be mitigated are listed. Furthermore the different risks are prioritized, which provides the foundation for the evaluation phase. Figure 3 shows an overview of how the different risks can be prioritized according to severity.

[[File:Risk matrix.png|400px|thumb|center|Figure 3:Risk Matrix for prioritizing risks bases on frequency and consequences in monetary terms
]].

'''Quantitative risk analysis''':Quantitative risk analysis is a process of quantifying various impacts of identified risks imposed on a specific project in context, by means of using their estimates from the qualitative analysis. The quantification process is conducted by allocating different probability distributions to respective risks and thus simulating hypothetical events (scenarios) of the identified risks<ref name=''Suhobokov''> ''Suhobokov, Alexander; Application of Monte Carlo Simulation Methods in Risk Management,Journal of Business Economics and Management 2007'' </ref>

*'''Phase 3: Evaluation''':
This phase involves the evaluation of the different risks by means of comparison in terms of severities and frequencies thus the establishment of tolerability levels. Organizations can in this phase chose to accept, try to avoid, transfer the risks or terminate the project if the risks are too high.

*'''Phase 4:Risk Monitoring and Control ''':
This phase involves the continuous monitoring, supervising and controlling of the different identified risks <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> . New risks that were not initially identified could also surface along a projects lifecycle, thus the necessity of continuously reassessing and revaluating risks. Figure 4 depicts a more detailed process of Risk management.
[[File:Risk analysis.png|400px|thumb|center|Figure 4: Different processes pertained in the four phases of risk management<ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>
]].

== Monte Carlo Simulation ==
In order to elaborate on what a Monte Carlo simulation is, the following terms must be understood in order to facilitate the comprehension of the concept of Monte Carlo simulation:

*'''Random variable (Stochastic variable)''':
A variable is any defined characteristic that is subjected to variation either due to natural and /or imposed factors e.g. height, age, temperature etc. A random variable on the other hand is any function that allocates a numerical value to each possible outcome<ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> i.e. a measurement or a count of a variable (characteristic) that varies randomly according to a certain pattern. Random variables can be categorized into two categories depending on the type of outcome from a certain pattern; Discrete or continuous. Discrete random variables are outcomes of random variables that are either finite or countably infinite, whereas if the set of possible outcomes of a random variable is an interval, then it is continuous <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> <ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> . E.g. when throwing two deices fifty times simultaneously as depicted in Figure 5, a player can’t predict what the outcome will be due to uncertainty, therefore when the outcome results into 5 and 6, the values are termed as random variables.

[[File:probability distribution.png|400px|thumb|center|Figure 5: Frequency table of dice throwing
]].

*'''Probability distribution''':
Probability is the chance of something in particular occurring e.g. when playing Russian roulette with a six chamber revolver loaded with one bullet, the probability of pulling the trigger thus igniting the bullet is P(X=bullet)=1/6. A distribution on the other hand is the listing of viable/intervals of values of a characteristic (variable). A probability distribution can thus be defined as the probability structure of a random variable <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> i.e. in other words the probability (chance) of a random variable taking on a value within a certain set of possible outcomes, whereas each of those outcomes have a certain probability of occurring. It should be noted that when dealing with discrete random variables, the probability distribution of the variable is referred to as the probability mass function, and on the other hand probability density function when dealing with continuous random variables <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> .

Thus a Monte Carlo simulation can be defined as a quantitative approach of quantifying risks, by means of utilizing a probability distribution <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. This is because risk is random and uncertain; therefore it can be classified as a random variable i.e. something that occurs by chance. Hence a Monte Carlo simulation of a particular project in context selects random variables from a given probability distribution of risk that is modeled by means of estimates. This is in order to figure out which risk has the highest certainty of occurring based on the probability distribution allocated to it. Furthermore, a Monte Carlo simulation enables the correlation of various risk factors, by means of incorporating a probability distribution for each conditional relationship<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref><ref name=''Hargreaves''> ''Hargreaves, John,Quantitative Risk Assessment in ERM, Enterprise Risk Management 2011'' </ref>. This thus facilitates the process of comprehending and assessing the various impacts of risks associated to a certain project and the pinpointing of ways in which they relate to each other.

== Methodology ==
A probability distribution model of identified risks are attained by means of acquiring historic or domain expert data (elaborated below) and thus conducting a data fitting, to observe which distribution model suits the various identified risks the most. A Monte Carlo simulation utilizes the probability distribution by means of selecting random variables (represents various risks) within a defined range of parameters. The process of selecting random variables is performed with multiple iterations to simulate different possible outcomes in order to find the risk with the highest certainty of occurring.
For the sake of simplifying and exemplifying; think of a company that is about to initiate a new project, the project group conducts a risk analysis thus identifying six risks that could affect their project during the qualitative risk analysis. The identified risks along with their estimates are then entered into a Monti Carlo simulation which then processes the data provided. An analogy in this case with regards to the Monti Carlo simulation is continuously throwing a six sided dice marked with R1, R2….. R6 (represents the identified risks) e.g. up to a thousand times and thus recording the frequencies at which they occur. All the six identified risk have an equal chance of occurring in this scenario i.e. 1/6 thus making the distribution to be applied, a uniform distribution, which is elaborated in the Data fitting section. This is a good way of understanding how a Monti Carlo simulation functions since it selects random variables from a given distribution and records the frequencies at which they occur. This iteration is done a couple of times to find the most likely risks. In order to conduct a Monti Carlo simulation of a given project, the following phases need to be undertaken:

=== Data source ===
It is crucial to attain applicable data when working with a Monte Carlo simulation. This is in order to grasp vital information embedded in the final results. The more precise the data inputs are, the more valuable data can be extracted. In other words trash in equals to trash out.
When dealing with data sourcing for a Monte Carlo simulation regarding risk, there are two forms of data that could be applied i.e. historic data (available) or domain expert knowledge (input from qualified experts)<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref>. A very good ex. of where domain expert knowledge is applied when conducting a risk analysis is within the construction industry. Experts that have worked within the field for years, thus acquiring knowledge and experience, use their foundation as a source for establishing estimates of price and time pertained to a certain building project. Historic data on the other hand can be sales data, cost of similar projects from the past or data from historic events etc.

=== Data fitting ===
Data fitting is the process of identifying the most appropriate probability distribution to simulate observed (historic) or defined (domain expert) data. E.g. by plotting a histogram over observed data and then plotting a distribution over it (normal distribution) as depicted in Figure 4 1 to visually inspect if the distribution fits. In many cases it can be very difficult to pinpoint the exact distribution to apply, when there is more than one distribution that can fit. Thus the necessity of goodness of fit, whereby the Chi-squared and Kolmogorov-Smirnoff test can be applied to test how good a distribution fits to a set of observed data (Gupta, 2013)
Common distributions applied in the simulation of risks in a Monti Carlo simulation are elaborated as follows:

*'''Normal Distribution''':
When utilizing the normal distribution two parameters must be defined, i.e. the mean (average of the data set) and the standard deviation (the difference between the various random variables from the mean). It is an unbounded distribution i.e. the possible outcomes of the random variables covers all possible values. The values centered about the middle (mean) of the distribution are most likely to occur with a probability of 68 % i.e. with one standard deviation from the mean. This can be seen in Figure 6, where the random variables around the mean have the highest frequencies. The normal distribution can be utilized to simulate risk of inflation by means of historic data thus mitigating the risk of budget overruns.

[[File:Normal distribution.png|400px|thumb|center|Figure 6: Illustration of data fitting and a normal distribution
]].

*'''Uniform Distribution''':
In this form of distribution all variables have an equal chance of occurring as initially mentioned in the dice analogy example of a Monte Carlo simulation. Two parameters have to be defined when utilizing the uniform distribution i.e. the minimum and maximum values as depicted in Figure 7.

[[File:Uniform distribution.png|400px|thumb|center|Figure 7: A uniform distribution, a =minimum and b = maximum
]].

*'''Triangle Distribution''':
This form of distribution is mostly applicable when dealing with domain expert data. When applying the triangle distribution three parameters have to be defined i.e. the minimum (optimistic), most likely (mode) and maximum (pessimistic) values<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref> as depicted in Figure 8. Triangle distributions are also bounded distributions since the possible outcomes of the random variables can only range within a defined interval

[[File:Triangle distribution.png|400px|thumb|center|Figure 8: Triangle distribution, a = minimum, b = mode and c = Maximum
]].

=== Iteration process ===
In this phase a Monte Carlo simulation software e.g. @Risk (works with excel), Latin hypercube, MATLAB etc. conducts a selecting process of random variables and identifies the most likely risks to occur based on the initially chosen probability distribution. Thereby simulating the likelihood of forecasted risks based on provided estimates.

== Conclusion ==

Monte Carlo Simulations are utilized in facilitating the process of identifying the most likely risks to occur during a project which could obstruct progress, by means of quantifying them. This thus facilitates the process of understanding and evaluating risks. Monte Carlo simulations are particularly fruitful when dealing with large projects since different risks can be modeled and a distribution can also be used in defining how the different risks relate to each other thus making it more realistic. The advantages and disadvantages pertained to Monti Carlo Simulations are as follows:

'''Advantages'''
* Reduced cost, due to the enablement of quantifying and mitigation risk prior to the implementation of its respective project.
* Acquired results are probabilistic thus apart from showing what eventually could happen, it also shows the likelihood.
* Easier to estimate intervals than to a specific value.

'''Disadvantage'''
* A certain degree of uncertainty on forecasted models, due to assumptions of the future. i.e. projection into the future, where there is no data availability therefore having to settle with estimations
* If the probability distribution is not suitable for a particular risk simulation, the output will not be useful i.e. garbage in garbage out.

== References ==

<references />

Monte Carlo Simulation of Risk

2014-12-02T14:42:34Z

Tallimac: /* Risk */

== Abstract ==

”To dare is to lose one’s footing momentarily. To not dare is to lose oneself” - Søren Kierkegaard

Staying dynamic is a means of survival in today’s modern society, thus the expression the burning platform. In order for companies to stay competitive and sustain their businesses, they have to either become cost leaders or product leaders i.e. innovative. In order to realize this, companies have to dive into new areas of business, explore new possibilities in terms of evolving their products and/or processes. During the process of exploring and maintaining dynamism, uncertainty is inevitable due to the respective volatile nature of the markets within which the diverse companies operate in. Companies can’t foresee the future, hence don’t know how their respective markets will react to their new ways of doing business, new products or on the other hand if they are resilient enough to sustain unexpected drawbacks when exploring new paths. Thus it can be concluded that risk is incorporated in the DNA of any project, program or portfolio management, therefore “Risk Management” is a necessity for companies to continuously embark when exploring new dimensions in order to mitigate risk and suppress their corresponding consequences.
There are several ways in which risk assessments can be conducted. This article provides a profound description of how to conduct a quantitative risk assessment by means of utilizing Monte Carlo simulations.

[[Category:''Project, Quantitative risk analysis, Monte Carlo Simulation'']]

== Risk ==
[[File:pure&speculative risk.png|400px|thumb|right|Figure 1: Categorize of risk<ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref>.
]].
It is basically inevitable not to associate risk and uncertainty to any human activity<ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> although they can vary from activity to activity, depending on what’s at stake. In literature, risk in management has been defined as the probability (chance) of an event occurring, which could eventually result (uncertainty) into a negative impact (consequence) on a particular project in context<ref name=''WANG & HUANG''> ''WANG, Xing-xia; HUANG, Jian-wen,Risk analysis of construction schedule based on Monte Carlo simulation,International Symposium on Computer Network and Multimedia Technology (CNMT 2009)'' </ref><ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> thus risk is an expected loss over time.

'''Risk = Probability of risk occurring ×Impact of risk occurring'''

Risk can be categorized as either pure or speculative <ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> <ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref> , as depicted in Figure 1 Regarding pure risk, there is no benefit or gain pertained to it, thus loss is the only possible outcome e.g. companies exposed to fraud or damage of assets etc. On the other hand, speculative risk can result into an uncertain degree of loss or gain e.g. a company can either gain or lose on investing in a new product, since there is a risk of market rejection. In this article emphasis is laid on speculative risk.

== Risk Management ==
When dealing with project, program or portfolio management it is crucial to understand that a lot of uncertainties (risks) are pertained to them, since their eventual benefits are projected into the future (vision/goal). These uncertainties occur randomly within the lifecycle of the different categorize of management, thus eventually resulting into delays, budget overruns and eventually terminations, if not managed hence the importance of risk management.

According to ISO 31000 <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management is the coordination of activities to direct and control an organization with regards to risk. In literature<ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management can be decomposed into four continuous phases i.e. risk - Identification, Analysis, Evaluation and Monitoring as depicted in Figure 2 and elaborated below. Before embarking on a risk management course, it is crucial to commence with a risk management plan. Risk management planning is the structuring and detailing of how the risk management process is going to be conducted throughout the lifecycle of a project <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. Subjects such as methodology, practices, roles and responsibilities, sequence and timing of activities are pertained to risk management planning <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>.
[[File:phases of risk management.png|400px|thumb|center|Figure2:Phases of risk management
]].

*'''Phase 1: Risk Identification ''':
This phase involves the pinpointing of potential risks that might eventually affect a particular project in context and their causes. It is usually conducted by domain experts by means of brain storming<ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>

*'''Phase 2: Risk Analysis''':
This phase involves the comprehension of the nature of the initially identified risks and an estimation of their consequences <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>, since risk have different severity degrees. Risk analysis can be sub divided into qualitative and quantitative risk analysis. Both approaches can be utilized in one project but in order to conduct a quantitative analysis, estimates from the qualitative analysis is needed. On the other hand risk analysts can chose to stop with a qualitative risk analysis if the project is not too big.

'''Qualitative risk analysis''':In a qualitative risk analysis descriptions and estimates (mostly in monetary terms) of the different consequences pertained to the different risks, estimates of their occurrence (frequency) and means by which they can be mitigated are listed. Furthermore the different risks are prioritized, which provides the foundation for the evaluation phase. Figure 3 shows an overview of how the different risks can be prioritized according to severity.

[[File:Risk matrix.png|400px|thumb|center|Figure 3:Risk Matrix for prioritizing risks bases on frequency and consequences in monetary terms
]].

'''Quantitative risk analysis''':Quantitative risk analysis is a process of quantifying various impacts of identified risks imposed on a specific project in context, by means of using their estimates from the qualitative analysis. The quantification process is conducted by allocating different probability distributions to respective risks and thus simulating hypothetical events (scenarios) of the identified risks<ref name=''Suhobokov''> ''Suhobokov, Alexander; Application of Monte Carlo Simulation Methods in Risk Management,Journal of Business Economics and Management 2007'' </ref>

*'''Phase 3: Evaluation''':
This phase involves the evaluation of the different risks by means of comparison in terms of severities and frequencies thus the establishment of tolerability levels. Organizations can in this phase chose to accept, try to avoid, transfer the risks or terminate the project if the risks are too high.

*'''Phase 4:Risk Monitoring and Control ''':
This phase involves the continuous monitoring, supervising and controlling of the different identified risks <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> . New risks that were not initially identified could also surface along a projects lifecycle, thus the necessity of continuously reassessing and revaluating risks. Figure 4 depicts a more detailed process of Risk management.
[[File:Risk analysis.png|400px|thumb|center|Figure 4: Different processes pertained in the four phases of risk management<ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>
]].

== Monte Carlo Simulation ==
In order to elaborate on what a Monte Carlo simulation is, the following terms must be understood in order to facilitate the comprehension of the concept of Monte Carlo simulation:

*'''Random variable (Stochastic variable)''':
A variable is any defined characteristic that is subjected to variation either due to natural and /or imposed factors e.g. height, age, temperature etc. A random variable on the other hand is any function that allocates a numerical value to each possible outcome<ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> i.e. a measurement or a count of a variable (characteristic) that varies randomly according to a certain pattern. Random variables can be categorized into two categories depending on the type of outcome from a certain pattern; Discrete or continuous. Discrete random variables are outcomes of random variables that are either finite or countably infinite, whereas if the set of possible outcomes of a random variable is an interval, then it is continuous <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> <ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> . E.g. when throwing two deices fifty times simultaneously as depicted in Figure 5, a player can’t predict what the outcome will be due to uncertainty, therefore when the outcome results into 5 and 6, the values are termed as random variables.

[[File:probability distribution.png|400px|thumb|center|Figure 5: Frequency table of dice throwing
]].

*'''Probability distribution''':
Probability is the chance of something in particular occurring e.g. when playing Russian roulette with a six chamber revolver loaded with one bullet, the probability of pulling the trigger thus igniting the bullet is P(X=bullet)=1/6. A distribution on the other hand is the listing of viable/intervals of values of a characteristic (variable). A probability distribution can thus be defined as the probability structure of a random variable <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> i.e. in other words the probability (chance) of a random variable taking on a value within a certain set of possible outcomes, whereas each of those outcomes have a certain probability of occurring. It should be noted that when dealing with discrete random variables, the probability distribution of the variable is referred to as the probability mass function, and on the other hand probability density function when dealing with continuous random variables <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> .

Thus a Monte Carlo simulation can be defined as a quantitative approach of quantifying risks, by means of utilizing a probability distribution <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. This is because risk is random and uncertain; therefore it can be classified as a random variable i.e. something that occurs by chance. Hence a Monte Carlo simulation of a particular project in context selects random variables from a given probability distribution of risk that is modeled by means of estimates. This is in order to figure out which risk has the highest certainty of occurring based on the probability distribution allocated to it. Furthermore, a Monte Carlo simulation enables the correlation of various risk factors, by means of incorporating a probability distribution for each conditional relationship<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref><ref name=''Hargreaves''> ''Hargreaves, John,Quantitative Risk Assessment in ERM, Enterprise Risk Management 2011'' </ref>. This thus facilitates the process of comprehending and assessing the various impacts of risks associated to a certain project and the pinpointing of ways in which they relate to each other.

== Methodology ==
A probability distribution model of identified risks are attained by means of acquiring historic or domain expert data (elaborated below) and thus conducting a data fitting, to observe which distribution model suits the various identified risks the most. A Monte Carlo simulation utilizes the probability distribution by means of selecting random variables (represents various risks) within a defined range of parameters. The process of selecting random variables is performed with multiple iterations to simulate different possible outcomes in order to find the risk with the highest certainty of occurring.
For the sake of simplifying and exemplifying; think of a company that is about to initiate a new project, the project group conducts a risk analysis thus identifying six risks that could affect their project during the qualitative risk analysis. The identified risks along with their estimates are then entered into a Monti Carlo simulation which then processes the data provided. An analogy in this case with regards to the Monti Carlo simulation is continuously throwing a six sided dice marked with R1, R2….. R6 (represents the identified risks) e.g. up to a thousand times and thus recording the frequencies at which they occur. All the six identified risk have an equal chance of occurring in this scenario i.e. 1/6 thus making the distribution to be applied, a uniform distribution, which is elaborated in the Data fitting section. This is a good way of understanding how a Monti Carlo simulation functions since it selects random variables from a given distribution and records the frequencies at which they occur. This iteration is done a couple of times to find the most likely risks. In order to conduct a Monti Carlo simulation of a given project, the following phases need to be undertaken:

=== Data source ===
It is crucial to attain applicable data when working with a Monte Carlo simulation. This is in order to grasp vital information embedded in the final results. The more precise the data inputs are, the more valuable data can be extracted. In other words trash in equals to trash out.
When dealing with data sourcing for a Monte Carlo simulation regarding risk, there are two forms of data that could be applied i.e. historic data (available) or domain expert knowledge (input from qualified experts)<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref>. A very good ex. of where domain expert knowledge is applied when conducting a risk analysis is within the construction industry. Experts that have worked within the field for years, thus acquiring knowledge and experience, use their foundation as a source for establishing estimates of price and time pertained to a certain building project. Historic data on the other hand can be sales data, cost of similar projects from the past or data from historic events etc.

=== Data fitting ===
Data fitting is the process of identifying the most appropriate probability distribution to simulate observed (historic) or defined (domain expert) data. E.g. by plotting a histogram over observed data and then plotting a distribution over it (normal distribution) as depicted in Figure 4 1 to visually inspect if the distribution fits. In many cases it can be very difficult to pinpoint the exact distribution to apply, when there is more than one distribution that can fit. Thus the necessity of goodness of fit, whereby the Chi-squared and Kolmogorov-Smirnoff test can be applied to test how good a distribution fits to a set of observed data (Gupta, 2013)
Common distributions applied in the simulation of risks in a Monti Carlo simulation are elaborated as follows:

*'''Normal Distribution''':
When utilizing the normal distribution two parameters must be defined, i.e. the mean (average of the data set) and the standard deviation (the difference between the various random variables from the mean). It is an unbounded distribution i.e. the possible outcomes of the random variables covers all possible values. The values centered about the middle (mean) of the distribution are most likely to occur with a probability of 68 % i.e. with one standard deviation from the mean. This can be seen in Figure 6, where the random variables around the mean have the highest frequencies. The normal distribution can be utilized to simulate risk of inflation by means of historic data thus mitigating the risk of budget overruns.

[[File:Normal distribution.png|400px|thumb|center|Figure 6: Illustration of data fitting and a normal distribution
]].

*'''Uniform Distribution''':
In this form of distribution all variables have an equal chance of occurring as initially mentioned in the dice analogy example of a Monte Carlo simulation. Two parameters have to be defined when utilizing the uniform distribution i.e. the minimum and maximum values as depicted in Figure 7.

[[File:Uniform distribution.png|400px|thumb|center|Figure 7: A uniform distribution, a =minimum and b = maximum
]].

*'''Triangle Distribution''':
This form of distribution is mostly applicable when dealing with domain expert data. When applying the triangle distribution three parameters have to be defined i.e. the minimum (optimistic), most likely (mode) and maximum (pessimistic) values<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref> as depicted in Figure 8. Triangle distributions are also bounded distributions since the possible outcomes of the random variables can only range within a defined interval

[[File:Triangle distribution.png|400px|thumb|center|Figure 8: Triangle distribution, a = minimum, b = mode and c = Maximum
]].

=== Iteration process ===
In this phase a Monte Carlo simulation software e.g. @Risk (works with excel), Latin hypercube, MATLAB etc. conducts a selecting process of random variables and identifies the most likely risks to occur based on the initially chosen probability distribution. Thereby simulating the likelihood of forecasted risks based on provided estimates.

== Conclusion ==

Monte Carlo Simulations are utilized in facilitating the process of identifying the most likely risks to occur during a project which could obstruct progress, by means of quantifying them. This thus facilitates the process of understanding and evaluating risks. Monte Carlo simulations are particularly fruitful when dealing with large projects since different risks can be modeled and a distribution can also be used in defining how the different risks relate to each other thus making it more realistic. The advantages and disadvantages pertained to Monti Carlo Simulations are as follows:

'''Advantages'''
* Reduced cost, due to the enablement of quantifying and mitigation risk prior to the implementation of its respective project.
* Acquired results are probabilistic thus apart from showing what eventually could happen, it also shows the likelihood.
* Easier to estimate intervals than to a specific value.

'''Disadvantage'''
* A certain degree of uncertainty on forecasted models, due to assumptions of the future. i.e. projection into the future, where there is no data availability therefore having to settle with estimations
* If the probability distribution is not suitable for a particular risk simulation, the output will not be useful i.e. garbage in garbage out.

== References ==

<references />

Monte Carlo Simulation of Risk

2014-12-02T14:41:05Z

Tallimac: /* Risk */

== Abstract ==

”To dare is to lose one’s footing momentarily. To not dare is to lose oneself” - Søren Kierkegaard

Staying dynamic is a means of survival in today’s modern society, thus the expression the burning platform. In order for companies to stay competitive and sustain their businesses, they have to either become cost leaders or product leaders i.e. innovative. In order to realize this, companies have to dive into new areas of business, explore new possibilities in terms of evolving their products and/or processes. During the process of exploring and maintaining dynamism, uncertainty is inevitable due to the respective volatile nature of the markets within which the diverse companies operate in. Companies can’t foresee the future, hence don’t know how their respective markets will react to their new ways of doing business, new products or on the other hand if they are resilient enough to sustain unexpected drawbacks when exploring new paths. Thus it can be concluded that risk is incorporated in the DNA of any project, program or portfolio management, therefore “Risk Management” is a necessity for companies to continuously embark when exploring new dimensions in order to mitigate risk and suppress their corresponding consequences.
There are several ways in which risk assessments can be conducted. This article provides a profound description of how to conduct a quantitative risk assessment by means of utilizing Monte Carlo simulations.

[[Category:''Project, Quantitative risk analysis, Monte Carlo Simulation'']]

== Risk ==
It is basically inevitable not to associate risk and uncertainty to any human activity<ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> although they can vary from activity to activity, depending on what’s at stake. In literature, risk in management has been defined as the probability (chance) of an event occurring, which could eventually result (uncertainty) into a negative impact (consequence) on a particular project in context<ref name=''WANG & HUANG''> ''WANG, Xing-xia; HUANG, Jian-wen,Risk analysis of construction schedule based on Monte Carlo simulation,International Symposium on Computer Network and Multimedia Technology (CNMT 2009)'' </ref><ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> thus risk is an expected loss over time.

'''Risk = Probability of risk occurring ×Impact of risk occurring'''

Risk can be categorized as either pure or speculative <ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> <ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref> , as depicted in Figure 1 Regarding pure risk, there is no benefit or gain pertained to it, thus loss is the only possible outcome e.g. companies exposed to fraud or damage of assets etc. On the other hand, speculative risk can result into an uncertain degree of loss or gain e.g. a company can either gain or lose on investing in a new product, since there is a risk of market rejection. In this article emphasis is laid on speculative risk.
[[File:pure&speculative risk.png|400px|thumb|right|Figure 1: Categorize of risk<ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref>.
]].

== Risk Management ==
When dealing with project, program or portfolio management it is crucial to understand that a lot of uncertainties (risks) are pertained to them, since their eventual benefits are projected into the future (vision/goal). These uncertainties occur randomly within the lifecycle of the different categorize of management, thus eventually resulting into delays, budget overruns and eventually terminations, if not managed hence the importance of risk management.

According to ISO 31000 <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management is the coordination of activities to direct and control an organization with regards to risk. In literature<ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management can be decomposed into four continuous phases i.e. risk - Identification, Analysis, Evaluation and Monitoring as depicted in Figure 2 and elaborated below. Before embarking on a risk management course, it is crucial to commence with a risk management plan. Risk management planning is the structuring and detailing of how the risk management process is going to be conducted throughout the lifecycle of a project <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. Subjects such as methodology, practices, roles and responsibilities, sequence and timing of activities are pertained to risk management planning <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>.
[[File:phases of risk management.png|400px|thumb|center|Figure2:Phases of risk management
]].

*'''Phase 1: Risk Identification ''':
This phase involves the pinpointing of potential risks that might eventually affect a particular project in context and their causes. It is usually conducted by domain experts by means of brain storming<ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>

*'''Phase 2: Risk Analysis''':
This phase involves the comprehension of the nature of the initially identified risks and an estimation of their consequences <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>, since risk have different severity degrees. Risk analysis can be sub divided into qualitative and quantitative risk analysis. Both approaches can be utilized in one project but in order to conduct a quantitative analysis, estimates from the qualitative analysis is needed. On the other hand risk analysts can chose to stop with a qualitative risk analysis if the project is not too big.

'''Qualitative risk analysis''':In a qualitative risk analysis descriptions and estimates (mostly in monetary terms) of the different consequences pertained to the different risks, estimates of their occurrence (frequency) and means by which they can be mitigated are listed. Furthermore the different risks are prioritized, which provides the foundation for the evaluation phase. Figure 3 shows an overview of how the different risks can be prioritized according to severity.

[[File:Risk matrix.png|400px|thumb|center|Figure 3:Risk Matrix for prioritizing risks bases on frequency and consequences in monetary terms
]].

'''Quantitative risk analysis''':Quantitative risk analysis is a process of quantifying various impacts of identified risks imposed on a specific project in context, by means of using their estimates from the qualitative analysis. The quantification process is conducted by allocating different probability distributions to respective risks and thus simulating hypothetical events (scenarios) of the identified risks<ref name=''Suhobokov''> ''Suhobokov, Alexander; Application of Monte Carlo Simulation Methods in Risk Management,Journal of Business Economics and Management 2007'' </ref>

*'''Phase 3: Evaluation''':
This phase involves the evaluation of the different risks by means of comparison in terms of severities and frequencies thus the establishment of tolerability levels. Organizations can in this phase chose to accept, try to avoid, transfer the risks or terminate the project if the risks are too high.

*'''Phase 4:Risk Monitoring and Control ''':
This phase involves the continuous monitoring, supervising and controlling of the different identified risks <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> . New risks that were not initially identified could also surface along a projects lifecycle, thus the necessity of continuously reassessing and revaluating risks. Figure 4 depicts a more detailed process of Risk management.
[[File:Risk analysis.png|400px|thumb|center|Figure 4: Different processes pertained in the four phases of risk management<ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>
]].

== Monte Carlo Simulation ==
In order to elaborate on what a Monte Carlo simulation is, the following terms must be understood in order to facilitate the comprehension of the concept of Monte Carlo simulation:

*'''Random variable (Stochastic variable)''':
A variable is any defined characteristic that is subjected to variation either due to natural and /or imposed factors e.g. height, age, temperature etc. A random variable on the other hand is any function that allocates a numerical value to each possible outcome<ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> i.e. a measurement or a count of a variable (characteristic) that varies randomly according to a certain pattern. Random variables can be categorized into two categories depending on the type of outcome from a certain pattern; Discrete or continuous. Discrete random variables are outcomes of random variables that are either finite or countably infinite, whereas if the set of possible outcomes of a random variable is an interval, then it is continuous <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> <ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> . E.g. when throwing two deices fifty times simultaneously as depicted in Figure 5, a player can’t predict what the outcome will be due to uncertainty, therefore when the outcome results into 5 and 6, the values are termed as random variables.

[[File:probability distribution.png|400px|thumb|center|Figure 5: Frequency table of dice throwing
]].

*'''Probability distribution''':
Probability is the chance of something in particular occurring e.g. when playing Russian roulette with a six chamber revolver loaded with one bullet, the probability of pulling the trigger thus igniting the bullet is P(X=bullet)=1/6. A distribution on the other hand is the listing of viable/intervals of values of a characteristic (variable). A probability distribution can thus be defined as the probability structure of a random variable <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> i.e. in other words the probability (chance) of a random variable taking on a value within a certain set of possible outcomes, whereas each of those outcomes have a certain probability of occurring. It should be noted that when dealing with discrete random variables, the probability distribution of the variable is referred to as the probability mass function, and on the other hand probability density function when dealing with continuous random variables <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> .

Thus a Monte Carlo simulation can be defined as a quantitative approach of quantifying risks, by means of utilizing a probability distribution <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. This is because risk is random and uncertain; therefore it can be classified as a random variable i.e. something that occurs by chance. Hence a Monte Carlo simulation of a particular project in context selects random variables from a given probability distribution of risk that is modeled by means of estimates. This is in order to figure out which risk has the highest certainty of occurring based on the probability distribution allocated to it. Furthermore, a Monte Carlo simulation enables the correlation of various risk factors, by means of incorporating a probability distribution for each conditional relationship<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref><ref name=''Hargreaves''> ''Hargreaves, John,Quantitative Risk Assessment in ERM, Enterprise Risk Management 2011'' </ref>. This thus facilitates the process of comprehending and assessing the various impacts of risks associated to a certain project and the pinpointing of ways in which they relate to each other.

== Methodology ==
A probability distribution model of identified risks are attained by means of acquiring historic or domain expert data (elaborated below) and thus conducting a data fitting, to observe which distribution model suits the various identified risks the most. A Monte Carlo simulation utilizes the probability distribution by means of selecting random variables (represents various risks) within a defined range of parameters. The process of selecting random variables is performed with multiple iterations to simulate different possible outcomes in order to find the risk with the highest certainty of occurring.
For the sake of simplifying and exemplifying; think of a company that is about to initiate a new project, the project group conducts a risk analysis thus identifying six risks that could affect their project during the qualitative risk analysis. The identified risks along with their estimates are then entered into a Monti Carlo simulation which then processes the data provided. An analogy in this case with regards to the Monti Carlo simulation is continuously throwing a six sided dice marked with R1, R2….. R6 (represents the identified risks) e.g. up to a thousand times and thus recording the frequencies at which they occur. All the six identified risk have an equal chance of occurring in this scenario i.e. 1/6 thus making the distribution to be applied, a uniform distribution, which is elaborated in the Data fitting section. This is a good way of understanding how a Monti Carlo simulation functions since it selects random variables from a given distribution and records the frequencies at which they occur. This iteration is done a couple of times to find the most likely risks. In order to conduct a Monti Carlo simulation of a given project, the following phases need to be undertaken:

=== Data source ===
It is crucial to attain applicable data when working with a Monte Carlo simulation. This is in order to grasp vital information embedded in the final results. The more precise the data inputs are, the more valuable data can be extracted. In other words trash in equals to trash out.
When dealing with data sourcing for a Monte Carlo simulation regarding risk, there are two forms of data that could be applied i.e. historic data (available) or domain expert knowledge (input from qualified experts)<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref>. A very good ex. of where domain expert knowledge is applied when conducting a risk analysis is within the construction industry. Experts that have worked within the field for years, thus acquiring knowledge and experience, use their foundation as a source for establishing estimates of price and time pertained to a certain building project. Historic data on the other hand can be sales data, cost of similar projects from the past or data from historic events etc.

=== Data fitting ===
Data fitting is the process of identifying the most appropriate probability distribution to simulate observed (historic) or defined (domain expert) data. E.g. by plotting a histogram over observed data and then plotting a distribution over it (normal distribution) as depicted in Figure 4 1 to visually inspect if the distribution fits. In many cases it can be very difficult to pinpoint the exact distribution to apply, when there is more than one distribution that can fit. Thus the necessity of goodness of fit, whereby the Chi-squared and Kolmogorov-Smirnoff test can be applied to test how good a distribution fits to a set of observed data (Gupta, 2013)
Common distributions applied in the simulation of risks in a Monti Carlo simulation are elaborated as follows:

*'''Normal Distribution''':
When utilizing the normal distribution two parameters must be defined, i.e. the mean (average of the data set) and the standard deviation (the difference between the various random variables from the mean). It is an unbounded distribution i.e. the possible outcomes of the random variables covers all possible values. The values centered about the middle (mean) of the distribution are most likely to occur with a probability of 68 % i.e. with one standard deviation from the mean. This can be seen in Figure 6, where the random variables around the mean have the highest frequencies. The normal distribution can be utilized to simulate risk of inflation by means of historic data thus mitigating the risk of budget overruns.

[[File:Normal distribution.png|400px|thumb|center|Figure 6: Illustration of data fitting and a normal distribution
]].

*'''Uniform Distribution''':
In this form of distribution all variables have an equal chance of occurring as initially mentioned in the dice analogy example of a Monte Carlo simulation. Two parameters have to be defined when utilizing the uniform distribution i.e. the minimum and maximum values as depicted in Figure 7.

[[File:Uniform distribution.png|400px|thumb|center|Figure 7: A uniform distribution, a =minimum and b = maximum
]].

*'''Triangle Distribution''':
This form of distribution is mostly applicable when dealing with domain expert data. When applying the triangle distribution three parameters have to be defined i.e. the minimum (optimistic), most likely (mode) and maximum (pessimistic) values<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref> as depicted in Figure 8. Triangle distributions are also bounded distributions since the possible outcomes of the random variables can only range within a defined interval

[[File:Triangle distribution.png|400px|thumb|center|Figure 8: Triangle distribution, a = minimum, b = mode and c = Maximum
]].

=== Iteration process ===
In this phase a Monte Carlo simulation software e.g. @Risk (works with excel), Latin hypercube, MATLAB etc. conducts a selecting process of random variables and identifies the most likely risks to occur based on the initially chosen probability distribution. Thereby simulating the likelihood of forecasted risks based on provided estimates.

== Conclusion ==

Monte Carlo Simulations are utilized in facilitating the process of identifying the most likely risks to occur during a project which could obstruct progress, by means of quantifying them. This thus facilitates the process of understanding and evaluating risks. Monte Carlo simulations are particularly fruitful when dealing with large projects since different risks can be modeled and a distribution can also be used in defining how the different risks relate to each other thus making it more realistic. The advantages and disadvantages pertained to Monti Carlo Simulations are as follows:

'''Advantages'''
* Reduced cost, due to the enablement of quantifying and mitigation risk prior to the implementation of its respective project.
* Acquired results are probabilistic thus apart from showing what eventually could happen, it also shows the likelihood.
* Easier to estimate intervals than to a specific value.

'''Disadvantage'''
* A certain degree of uncertainty on forecasted models, due to assumptions of the future. i.e. projection into the future, where there is no data availability therefore having to settle with estimations
* If the probability distribution is not suitable for a particular risk simulation, the output will not be useful i.e. garbage in garbage out.

== References ==

<references />

Monte Carlo Simulation of Risk

2014-12-02T14:40:19Z

Tallimac: /* Risk */

== Abstract ==

”To dare is to lose one’s footing momentarily. To not dare is to lose oneself” - Søren Kierkegaard

Staying dynamic is a means of survival in today’s modern society, thus the expression the burning platform. In order for companies to stay competitive and sustain their businesses, they have to either become cost leaders or product leaders i.e. innovative. In order to realize this, companies have to dive into new areas of business, explore new possibilities in terms of evolving their products and/or processes. During the process of exploring and maintaining dynamism, uncertainty is inevitable due to the respective volatile nature of the markets within which the diverse companies operate in. Companies can’t foresee the future, hence don’t know how their respective markets will react to their new ways of doing business, new products or on the other hand if they are resilient enough to sustain unexpected drawbacks when exploring new paths. Thus it can be concluded that risk is incorporated in the DNA of any project, program or portfolio management, therefore “Risk Management” is a necessity for companies to continuously embark when exploring new dimensions in order to mitigate risk and suppress their corresponding consequences.
There are several ways in which risk assessments can be conducted. This article provides a profound description of how to conduct a quantitative risk assessment by means of utilizing Monte Carlo simulations.

[[Category:''Project, Quantitative risk analysis, Monte Carlo Simulation'']]

== Risk ==
It is basically inevitable not to associate risk and uncertainty to any human activity<ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> although they can vary from activity to activity, depending on what’s at stake. In literature, risk in management has been defined as the probability (chance) of an event occurring, which could eventually result (uncertainty) into a negative impact (consequence) on a particular project in context<ref name=''WANG & HUANG''> ''WANG, Xing-xia; HUANG, Jian-wen,Risk analysis of construction schedule based on Monte Carlo simulation,International Symposium on Computer Network and Multimedia Technology (CNMT 2009)'' </ref><ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> thus risk is an expected loss over time.

'''Risk = Probability of risk occurring ×Impact of risk occurring'''

Risk can be categorized as either pure or speculative <ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> <ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref> , as depicted in Figure 1 Regarding pure risk, there is no benefit or gain pertained to it, thus loss is the only possible outcome e.g. companies exposed to fraud or damage of assets etc. On the other hand, speculative risk can result into an uncertain degree of loss or gain e.g. a company can either gain or lose on investing in a new product, since there is a risk of market rejection. In this article emphasis is laid on speculative risk.
[[File:pure&speculative risk.png|400px|thumb|center|Figure 1: Categorize of risk<ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref>.
]].

== Risk Management ==
When dealing with project, program or portfolio management it is crucial to understand that a lot of uncertainties (risks) are pertained to them, since their eventual benefits are projected into the future (vision/goal). These uncertainties occur randomly within the lifecycle of the different categorize of management, thus eventually resulting into delays, budget overruns and eventually terminations, if not managed hence the importance of risk management.

According to ISO 31000 <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management is the coordination of activities to direct and control an organization with regards to risk. In literature<ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management can be decomposed into four continuous phases i.e. risk - Identification, Analysis, Evaluation and Monitoring as depicted in Figure 2 and elaborated below. Before embarking on a risk management course, it is crucial to commence with a risk management plan. Risk management planning is the structuring and detailing of how the risk management process is going to be conducted throughout the lifecycle of a project <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. Subjects such as methodology, practices, roles and responsibilities, sequence and timing of activities are pertained to risk management planning <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>.
[[File:phases of risk management.png|400px|thumb|center|Figure2:Phases of risk management
]].

*'''Phase 1: Risk Identification ''':
This phase involves the pinpointing of potential risks that might eventually affect a particular project in context and their causes. It is usually conducted by domain experts by means of brain storming<ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>

*'''Phase 2: Risk Analysis''':
This phase involves the comprehension of the nature of the initially identified risks and an estimation of their consequences <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>, since risk have different severity degrees. Risk analysis can be sub divided into qualitative and quantitative risk analysis. Both approaches can be utilized in one project but in order to conduct a quantitative analysis, estimates from the qualitative analysis is needed. On the other hand risk analysts can chose to stop with a qualitative risk analysis if the project is not too big.

'''Qualitative risk analysis''':In a qualitative risk analysis descriptions and estimates (mostly in monetary terms) of the different consequences pertained to the different risks, estimates of their occurrence (frequency) and means by which they can be mitigated are listed. Furthermore the different risks are prioritized, which provides the foundation for the evaluation phase. Figure 3 shows an overview of how the different risks can be prioritized according to severity.

[[File:Risk matrix.png|400px|thumb|center|Figure 3:Risk Matrix for prioritizing risks bases on frequency and consequences in monetary terms
]].

'''Quantitative risk analysis''':Quantitative risk analysis is a process of quantifying various impacts of identified risks imposed on a specific project in context, by means of using their estimates from the qualitative analysis. The quantification process is conducted by allocating different probability distributions to respective risks and thus simulating hypothetical events (scenarios) of the identified risks<ref name=''Suhobokov''> ''Suhobokov, Alexander; Application of Monte Carlo Simulation Methods in Risk Management,Journal of Business Economics and Management 2007'' </ref>

*'''Phase 3: Evaluation''':
This phase involves the evaluation of the different risks by means of comparison in terms of severities and frequencies thus the establishment of tolerability levels. Organizations can in this phase chose to accept, try to avoid, transfer the risks or terminate the project if the risks are too high.

*'''Phase 4:Risk Monitoring and Control ''':
This phase involves the continuous monitoring, supervising and controlling of the different identified risks <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> . New risks that were not initially identified could also surface along a projects lifecycle, thus the necessity of continuously reassessing and revaluating risks. Figure 4 depicts a more detailed process of Risk management.
[[File:Risk analysis.png|400px|thumb|center|Figure 4: Different processes pertained in the four phases of risk management<ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>
]].

== Monte Carlo Simulation ==
In order to elaborate on what a Monte Carlo simulation is, the following terms must be understood in order to facilitate the comprehension of the concept of Monte Carlo simulation:

*'''Random variable (Stochastic variable)''':
A variable is any defined characteristic that is subjected to variation either due to natural and /or imposed factors e.g. height, age, temperature etc. A random variable on the other hand is any function that allocates a numerical value to each possible outcome<ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> i.e. a measurement or a count of a variable (characteristic) that varies randomly according to a certain pattern. Random variables can be categorized into two categories depending on the type of outcome from a certain pattern; Discrete or continuous. Discrete random variables are outcomes of random variables that are either finite or countably infinite, whereas if the set of possible outcomes of a random variable is an interval, then it is continuous <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> <ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> . E.g. when throwing two deices fifty times simultaneously as depicted in Figure 5, a player can’t predict what the outcome will be due to uncertainty, therefore when the outcome results into 5 and 6, the values are termed as random variables.

[[File:probability distribution.png|400px|thumb|center|Figure 5: Frequency table of dice throwing
]].

*'''Probability distribution''':
Probability is the chance of something in particular occurring e.g. when playing Russian roulette with a six chamber revolver loaded with one bullet, the probability of pulling the trigger thus igniting the bullet is P(X=bullet)=1/6. A distribution on the other hand is the listing of viable/intervals of values of a characteristic (variable). A probability distribution can thus be defined as the probability structure of a random variable <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> i.e. in other words the probability (chance) of a random variable taking on a value within a certain set of possible outcomes, whereas each of those outcomes have a certain probability of occurring. It should be noted that when dealing with discrete random variables, the probability distribution of the variable is referred to as the probability mass function, and on the other hand probability density function when dealing with continuous random variables <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> .

Thus a Monte Carlo simulation can be defined as a quantitative approach of quantifying risks, by means of utilizing a probability distribution <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. This is because risk is random and uncertain; therefore it can be classified as a random variable i.e. something that occurs by chance. Hence a Monte Carlo simulation of a particular project in context selects random variables from a given probability distribution of risk that is modeled by means of estimates. This is in order to figure out which risk has the highest certainty of occurring based on the probability distribution allocated to it. Furthermore, a Monte Carlo simulation enables the correlation of various risk factors, by means of incorporating a probability distribution for each conditional relationship<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref><ref name=''Hargreaves''> ''Hargreaves, John,Quantitative Risk Assessment in ERM, Enterprise Risk Management 2011'' </ref>. This thus facilitates the process of comprehending and assessing the various impacts of risks associated to a certain project and the pinpointing of ways in which they relate to each other.

== Methodology ==
A probability distribution model of identified risks are attained by means of acquiring historic or domain expert data (elaborated below) and thus conducting a data fitting, to observe which distribution model suits the various identified risks the most. A Monte Carlo simulation utilizes the probability distribution by means of selecting random variables (represents various risks) within a defined range of parameters. The process of selecting random variables is performed with multiple iterations to simulate different possible outcomes in order to find the risk with the highest certainty of occurring.
For the sake of simplifying and exemplifying; think of a company that is about to initiate a new project, the project group conducts a risk analysis thus identifying six risks that could affect their project during the qualitative risk analysis. The identified risks along with their estimates are then entered into a Monti Carlo simulation which then processes the data provided. An analogy in this case with regards to the Monti Carlo simulation is continuously throwing a six sided dice marked with R1, R2….. R6 (represents the identified risks) e.g. up to a thousand times and thus recording the frequencies at which they occur. All the six identified risk have an equal chance of occurring in this scenario i.e. 1/6 thus making the distribution to be applied, a uniform distribution, which is elaborated in the Data fitting section. This is a good way of understanding how a Monti Carlo simulation functions since it selects random variables from a given distribution and records the frequencies at which they occur. This iteration is done a couple of times to find the most likely risks. In order to conduct a Monti Carlo simulation of a given project, the following phases need to be undertaken:

=== Data source ===
It is crucial to attain applicable data when working with a Monte Carlo simulation. This is in order to grasp vital information embedded in the final results. The more precise the data inputs are, the more valuable data can be extracted. In other words trash in equals to trash out.
When dealing with data sourcing for a Monte Carlo simulation regarding risk, there are two forms of data that could be applied i.e. historic data (available) or domain expert knowledge (input from qualified experts)<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref>. A very good ex. of where domain expert knowledge is applied when conducting a risk analysis is within the construction industry. Experts that have worked within the field for years, thus acquiring knowledge and experience, use their foundation as a source for establishing estimates of price and time pertained to a certain building project. Historic data on the other hand can be sales data, cost of similar projects from the past or data from historic events etc.

=== Data fitting ===
Data fitting is the process of identifying the most appropriate probability distribution to simulate observed (historic) or defined (domain expert) data. E.g. by plotting a histogram over observed data and then plotting a distribution over it (normal distribution) as depicted in Figure 4 1 to visually inspect if the distribution fits. In many cases it can be very difficult to pinpoint the exact distribution to apply, when there is more than one distribution that can fit. Thus the necessity of goodness of fit, whereby the Chi-squared and Kolmogorov-Smirnoff test can be applied to test how good a distribution fits to a set of observed data (Gupta, 2013)
Common distributions applied in the simulation of risks in a Monti Carlo simulation are elaborated as follows:

*'''Normal Distribution''':
When utilizing the normal distribution two parameters must be defined, i.e. the mean (average of the data set) and the standard deviation (the difference between the various random variables from the mean). It is an unbounded distribution i.e. the possible outcomes of the random variables covers all possible values. The values centered about the middle (mean) of the distribution are most likely to occur with a probability of 68 % i.e. with one standard deviation from the mean. This can be seen in Figure 6, where the random variables around the mean have the highest frequencies. The normal distribution can be utilized to simulate risk of inflation by means of historic data thus mitigating the risk of budget overruns.

[[File:Normal distribution.png|400px|thumb|center|Figure 6: Illustration of data fitting and a normal distribution
]].

*'''Uniform Distribution''':
In this form of distribution all variables have an equal chance of occurring as initially mentioned in the dice analogy example of a Monte Carlo simulation. Two parameters have to be defined when utilizing the uniform distribution i.e. the minimum and maximum values as depicted in Figure 7.

[[File:Uniform distribution.png|400px|thumb|center|Figure 7: A uniform distribution, a =minimum and b = maximum
]].

*'''Triangle Distribution''':
This form of distribution is mostly applicable when dealing with domain expert data. When applying the triangle distribution three parameters have to be defined i.e. the minimum (optimistic), most likely (mode) and maximum (pessimistic) values<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref> as depicted in Figure 8. Triangle distributions are also bounded distributions since the possible outcomes of the random variables can only range within a defined interval

[[File:Triangle distribution.png|400px|thumb|center|Figure 8: Triangle distribution, a = minimum, b = mode and c = Maximum
]].

=== Iteration process ===
In this phase a Monte Carlo simulation software e.g. @Risk (works with excel), Latin hypercube, MATLAB etc. conducts a selecting process of random variables and identifies the most likely risks to occur based on the initially chosen probability distribution. Thereby simulating the likelihood of forecasted risks based on provided estimates.

== Conclusion ==

Monte Carlo Simulations are utilized in facilitating the process of identifying the most likely risks to occur during a project which could obstruct progress, by means of quantifying them. This thus facilitates the process of understanding and evaluating risks. Monte Carlo simulations are particularly fruitful when dealing with large projects since different risks can be modeled and a distribution can also be used in defining how the different risks relate to each other thus making it more realistic. The advantages and disadvantages pertained to Monti Carlo Simulations are as follows:

'''Advantages'''
* Reduced cost, due to the enablement of quantifying and mitigation risk prior to the implementation of its respective project.
* Acquired results are probabilistic thus apart from showing what eventually could happen, it also shows the likelihood.
* Easier to estimate intervals than to a specific value.

'''Disadvantage'''
* A certain degree of uncertainty on forecasted models, due to assumptions of the future. i.e. projection into the future, where there is no data availability therefore having to settle with estimations
* If the probability distribution is not suitable for a particular risk simulation, the output will not be useful i.e. garbage in garbage out.

== References ==

<references />

Monte Carlo Simulation of Risk

2014-12-02T14:39:20Z

Tallimac: /* Risk */

== Abstract ==

”To dare is to lose one’s footing momentarily. To not dare is to lose oneself” - Søren Kierkegaard

Staying dynamic is a means of survival in today’s modern society, thus the expression the burning platform. In order for companies to stay competitive and sustain their businesses, they have to either become cost leaders or product leaders i.e. innovative. In order to realize this, companies have to dive into new areas of business, explore new possibilities in terms of evolving their products and/or processes. During the process of exploring and maintaining dynamism, uncertainty is inevitable due to the respective volatile nature of the markets within which the diverse companies operate in. Companies can’t foresee the future, hence don’t know how their respective markets will react to their new ways of doing business, new products or on the other hand if they are resilient enough to sustain unexpected drawbacks when exploring new paths. Thus it can be concluded that risk is incorporated in the DNA of any project, program or portfolio management, therefore “Risk Management” is a necessity for companies to continuously embark when exploring new dimensions in order to mitigate risk and suppress their corresponding consequences.
There are several ways in which risk assessments can be conducted. This article provides a profound description of how to conduct a quantitative risk assessment by means of utilizing Monte Carlo simulations.

[[Category:''Project, Quantitative risk analysis, Monte Carlo Simulation'']]

== Risk ==
It is basically inevitable not to associate risk and uncertainty to any human activity<ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> although they can vary from activity to activity, depending on what’s at stake. In literature, risk in management has been defined as the probability (chance) of an event occurring, which could eventually result (uncertainty) into a negative impact (consequence) on a particular project in context<ref name=''WANG & HUANG''> ''WANG, Xing-xia; HUANG, Jian-wen,Risk analysis of construction schedule based on Monte Carlo simulation,International Symposium on Computer Network and Multimedia Technology (CNMT 2009)'' </ref><ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> thus risk is an expected loss over time.

'''Risk = Probability of risk occurring ×Impact of risk occurring'''

Risk can be categorized as either pure or speculative <ref name=''Hertz''> ''Hertz David B. & Thomas Howard,Risk analysis and its application 1983'' </ref> <ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref> , as depicted in Figure 1 Regarding pure risk, there is no benefit or gain pertained to it, thus loss is the only possible outcome e.g. companies exposed to fraud or damage of assets etc. On the other hand, speculative risk can result into an uncertain degree of loss or gain e.g. a company can either gain or lose on investing in a new product, since there is a risk of market rejection. In this article emphasis is laid on speculative risk.
[[File:pure&speculative risk.png|400px|thumb|right|Figure 1: Categorize of risk<ref name=''Gupta''> ''Gupta, Aparna,Risk Management and Simulation 2013'' </ref>.
]].

== Risk Management ==
When dealing with project, program or portfolio management it is crucial to understand that a lot of uncertainties (risks) are pertained to them, since their eventual benefits are projected into the future (vision/goal). These uncertainties occur randomly within the lifecycle of the different categorize of management, thus eventually resulting into delays, budget overruns and eventually terminations, if not managed hence the importance of risk management.

According to ISO 31000 <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management is the coordination of activities to direct and control an organization with regards to risk. In literature<ref name=''Nemuth''> ''Nemuth, Dr.-Ing. Tilo,Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry,6th International Probabilistic Workshop 2008'' </ref> <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> risk management can be decomposed into four continuous phases i.e. risk - Identification, Analysis, Evaluation and Monitoring as depicted in Figure 2 and elaborated below. Before embarking on a risk management course, it is crucial to commence with a risk management plan. Risk management planning is the structuring and detailing of how the risk management process is going to be conducted throughout the lifecycle of a project <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. Subjects such as methodology, practices, roles and responsibilities, sequence and timing of activities are pertained to risk management planning <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>.
[[File:phases of risk management.png|400px|thumb|center|Figure2:Phases of risk management
]].

*'''Phase 1: Risk Identification ''':
This phase involves the pinpointing of potential risks that might eventually affect a particular project in context and their causes. It is usually conducted by domain experts by means of brain storming<ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>

*'''Phase 2: Risk Analysis''':
This phase involves the comprehension of the nature of the initially identified risks and an estimation of their consequences <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>, since risk have different severity degrees. Risk analysis can be sub divided into qualitative and quantitative risk analysis. Both approaches can be utilized in one project but in order to conduct a quantitative analysis, estimates from the qualitative analysis is needed. On the other hand risk analysts can chose to stop with a qualitative risk analysis if the project is not too big.

'''Qualitative risk analysis''':In a qualitative risk analysis descriptions and estimates (mostly in monetary terms) of the different consequences pertained to the different risks, estimates of their occurrence (frequency) and means by which they can be mitigated are listed. Furthermore the different risks are prioritized, which provides the foundation for the evaluation phase. Figure 3 shows an overview of how the different risks can be prioritized according to severity.

[[File:Risk matrix.png|400px|thumb|center|Figure 3:Risk Matrix for prioritizing risks bases on frequency and consequences in monetary terms
]].

'''Quantitative risk analysis''':Quantitative risk analysis is a process of quantifying various impacts of identified risks imposed on a specific project in context, by means of using their estimates from the qualitative analysis. The quantification process is conducted by allocating different probability distributions to respective risks and thus simulating hypothetical events (scenarios) of the identified risks<ref name=''Suhobokov''> ''Suhobokov, Alexander; Application of Monte Carlo Simulation Methods in Risk Management,Journal of Business Economics and Management 2007'' </ref>

*'''Phase 3: Evaluation''':
This phase involves the evaluation of the different risks by means of comparison in terms of severities and frequencies thus the establishment of tolerability levels. Organizations can in this phase chose to accept, try to avoid, transfer the risks or terminate the project if the risks are too high.

*'''Phase 4:Risk Monitoring and Control ''':
This phase involves the continuous monitoring, supervising and controlling of the different identified risks <ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref> . New risks that were not initially identified could also surface along a projects lifecycle, thus the necessity of continuously reassessing and revaluating risks. Figure 4 depicts a more detailed process of Risk management.
[[File:Risk analysis.png|400px|thumb|center|Figure 4: Different processes pertained in the four phases of risk management<ref name=''Danish Standards''> ''Danish Standards,ISO 31000 Risk management - Principles and guidelines 2009'' </ref>
]].

== Monte Carlo Simulation ==
In order to elaborate on what a Monte Carlo simulation is, the following terms must be understood in order to facilitate the comprehension of the concept of Monte Carlo simulation:

*'''Random variable (Stochastic variable)''':
A variable is any defined characteristic that is subjected to variation either due to natural and /or imposed factors e.g. height, age, temperature etc. A random variable on the other hand is any function that allocates a numerical value to each possible outcome<ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> i.e. a measurement or a count of a variable (characteristic) that varies randomly according to a certain pattern. Random variables can be categorized into two categories depending on the type of outcome from a certain pattern; Discrete or continuous. Discrete random variables are outcomes of random variables that are either finite or countably infinite, whereas if the set of possible outcomes of a random variable is an interval, then it is continuous <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> <ref name=''Johnson''> ''Johnson, Richard A; Freund, John; Miller, Irwin,Probability and Statistics for Engineers 2011'' </ref> . E.g. when throwing two deices fifty times simultaneously as depicted in Figure 5, a player can’t predict what the outcome will be due to uncertainty, therefore when the outcome results into 5 and 6, the values are termed as random variables.

[[File:probability distribution.png|400px|thumb|center|Figure 5: Frequency table of dice throwing
]].

*'''Probability distribution''':
Probability is the chance of something in particular occurring e.g. when playing Russian roulette with a six chamber revolver loaded with one bullet, the probability of pulling the trigger thus igniting the bullet is P(X=bullet)=1/6. A distribution on the other hand is the listing of viable/intervals of values of a characteristic (variable). A probability distribution can thus be defined as the probability structure of a random variable <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> i.e. in other words the probability (chance) of a random variable taking on a value within a certain set of possible outcomes, whereas each of those outcomes have a certain probability of occurring. It should be noted that when dealing with discrete random variables, the probability distribution of the variable is referred to as the probability mass function, and on the other hand probability density function when dealing with continuous random variables <ref name=''Montgomery''> ''Montgomery, Douglas c,Design and Analysis of Experiments 2012'' </ref> .

Thus a Monte Carlo simulation can be defined as a quantitative approach of quantifying risks, by means of utilizing a probability distribution <ref name=''Tysiak''> ''Tysiak, Wolfgang; Sereseanu, Alexander,International Journal of Computing 2010'' </ref>. This is because risk is random and uncertain; therefore it can be classified as a random variable i.e. something that occurs by chance. Hence a Monte Carlo simulation of a particular project in context selects random variables from a given probability distribution of risk that is modeled by means of estimates. This is in order to figure out which risk has the highest certainty of occurring based on the probability distribution allocated to it. Furthermore, a Monte Carlo simulation enables the correlation of various risk factors, by means of incorporating a probability distribution for each conditional relationship<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref><ref name=''Hargreaves''> ''Hargreaves, John,Quantitative Risk Assessment in ERM, Enterprise Risk Management 2011'' </ref>. This thus facilitates the process of comprehending and assessing the various impacts of risks associated to a certain project and the pinpointing of ways in which they relate to each other.

== Methodology ==
A probability distribution model of identified risks are attained by means of acquiring historic or domain expert data (elaborated below) and thus conducting a data fitting, to observe which distribution model suits the various identified risks the most. A Monte Carlo simulation utilizes the probability distribution by means of selecting random variables (represents various risks) within a defined range of parameters. The process of selecting random variables is performed with multiple iterations to simulate different possible outcomes in order to find the risk with the highest certainty of occurring.
For the sake of simplifying and exemplifying; think of a company that is about to initiate a new project, the project group conducts a risk analysis thus identifying six risks that could affect their project during the qualitative risk analysis. The identified risks along with their estimates are then entered into a Monti Carlo simulation which then processes the data provided. An analogy in this case with regards to the Monti Carlo simulation is continuously throwing a six sided dice marked with R1, R2….. R6 (represents the identified risks) e.g. up to a thousand times and thus recording the frequencies at which they occur. All the six identified risk have an equal chance of occurring in this scenario i.e. 1/6 thus making the distribution to be applied, a uniform distribution, which is elaborated in the Data fitting section. This is a good way of understanding how a Monti Carlo simulation functions since it selects random variables from a given distribution and records the frequencies at which they occur. This iteration is done a couple of times to find the most likely risks. In order to conduct a Monti Carlo simulation of a given project, the following phases need to be undertaken:

=== Data source ===
It is crucial to attain applicable data when working with a Monte Carlo simulation. This is in order to grasp vital information embedded in the final results. The more precise the data inputs are, the more valuable data can be extracted. In other words trash in equals to trash out.
When dealing with data sourcing for a Monte Carlo simulation regarding risk, there are two forms of data that could be applied i.e. historic data (available) or domain expert knowledge (input from qualified experts)<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref>. A very good ex. of where domain expert knowledge is applied when conducting a risk analysis is within the construction industry. Experts that have worked within the field for years, thus acquiring knowledge and experience, use their foundation as a source for establishing estimates of price and time pertained to a certain building project. Historic data on the other hand can be sales data, cost of similar projects from the past or data from historic events etc.

=== Data fitting ===
Data fitting is the process of identifying the most appropriate probability distribution to simulate observed (historic) or defined (domain expert) data. E.g. by plotting a histogram over observed data and then plotting a distribution over it (normal distribution) as depicted in Figure 4 1 to visually inspect if the distribution fits. In many cases it can be very difficult to pinpoint the exact distribution to apply, when there is more than one distribution that can fit. Thus the necessity of goodness of fit, whereby the Chi-squared and Kolmogorov-Smirnoff test can be applied to test how good a distribution fits to a set of observed data (Gupta, 2013)
Common distributions applied in the simulation of risks in a Monti Carlo simulation are elaborated as follows:

*'''Normal Distribution''':
When utilizing the normal distribution two parameters must be defined, i.e. the mean (average of the data set) and the standard deviation (the difference between the various random variables from the mean). It is an unbounded distribution i.e. the possible outcomes of the random variables covers all possible values. The values centered about the middle (mean) of the distribution are most likely to occur with a probability of 68 % i.e. with one standard deviation from the mean. This can be seen in Figure 6, where the random variables around the mean have the highest frequencies. The normal distribution can be utilized to simulate risk of inflation by means of historic data thus mitigating the risk of budget overruns.

[[File:Normal distribution.png|400px|thumb|center|Figure 6: Illustration of data fitting and a normal distribution
]].

*'''Uniform Distribution''':
In this form of distribution all variables have an equal chance of occurring as initially mentioned in the dice analogy example of a Monte Carlo simulation. Two parameters have to be defined when utilizing the uniform distribution i.e. the minimum and maximum values as depicted in Figure 7.

[[File:Uniform distribution.png|400px|thumb|center|Figure 7: A uniform distribution, a =minimum and b = maximum
]].

*'''Triangle Distribution''':
This form of distribution is mostly applicable when dealing with domain expert data. When applying the triangle distribution three parameters have to be defined i.e. the minimum (optimistic), most likely (mode) and maximum (pessimistic) values<ref name=''McCabe''> ''McCabe, Brenda,Monte carlo simulation for schedule risks, Winter Simulation Conference Proceedings, Volume 2 2003'' </ref> as depicted in Figure 8. Triangle distributions are also bounded distributions since the possible outcomes of the random variables can only range within a defined interval

[[File:Triangle distribution.png|400px|thumb|center|Figure 8: Triangle distribution, a = minimum, b = mode and c = Maximum
]].

=== Iteration process ===
In this phase a Monte Carlo simulation software e.g. @Risk (works with excel), Latin hypercube, MATLAB etc. conducts a selecting process of random variables and identifies the most likely risks to occur based on the initially chosen probability distribution. Thereby simulating the likelihood of forecasted risks based on provided estimates.

== Conclusion ==

Monte Carlo Simulations are utilized in facilitating the process of identifying the most likely risks to occur during a project which could obstruct progress, by means of quantifying them. This thus facilitates the process of understanding and evaluating risks. Monte Carlo simulations are particularly fruitful when dealing with large projects since different risks can be modeled and a distribution can also be used in defining how the different risks relate to each other thus making it more realistic. The advantages and disadvantages pertained to Monti Carlo Simulations are as follows:

'''Advantages'''
* Reduced cost, due to the enablement of quantifying and mitigation risk prior to the implementation of its respective project.
* Acquired results are probabilistic thus apart from showing what eventually could happen, it also shows the likelihood.
* Easier to estimate intervals than to a specific value.

'''Disadvantage'''
* A certain degree of uncertainty on forecasted models, due to assumptions of the future. i.e. projection into the future, where there is no data availability therefore having to settle with estimations
* If the probability distribution is not suitable for a particular risk simulation, the output will not be useful i.e. garbage in garbage out.

== References ==

<references />

Bubble Diagram

2014-12-01T22:48:32Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class">Cooper, R.G., Edgett, S., Kleinschmidt, E. Portfolio management for new product development: results of an industry practices study 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">Cooper, R.G., Edgett, S., Kleinschmidt, E. New Product Portfolio Management: Practices and Performance 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

=Discussion=
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau">Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. Managing a portfolio of interdependent new product candidates in the pharmaceutical industry 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

==Alternatives==
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools for example a scoring model.

===Disclaimer for bias information===
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:48:17Z

Tallimac: /* Discussion */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class">Cooper, R.G., Edgett, S., Kleinschmidt, E. Portfolio management for new product development: results of an industry practices study 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">Cooper, R.G., Edgett, S., Kleinschmidt, E. New Product Portfolio Management: Practices and Performance 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

=Discussion=
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau">Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. Managing a portfolio of interdependent new product candidates in the pharmaceutical industry 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools for example a scoring model.

===Disclaimer for bias information===
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:42:45Z

Tallimac: /* Dsiclaimer for bias information */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class">Cooper, R.G., Edgett, S., Kleinschmidt, E. Portfolio management for new product development: results of an industry practices study 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">Cooper, R.G., Edgett, S., Kleinschmidt, E. New Product Portfolio Management: Practices and Performance 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau">Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. Managing a portfolio of interdependent new product candidates in the pharmaceutical industry 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools for example a scoring model.

===Disclaimer for bias information===
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:42:36Z

Tallimac: /* Dsiclaimer for bias information */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class">Cooper, R.G., Edgett, S., Kleinschmidt, E. Portfolio management for new product development: results of an industry practices study 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">Cooper, R.G., Edgett, S., Kleinschmidt, E. New Product Portfolio Management: Practices and Performance 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau">Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. Managing a portfolio of interdependent new product candidates in the pharmaceutical industry 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools for example a scoring model.

===Dsiclaimer for bias information===
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:42:19Z

Tallimac:

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class">Cooper, R.G., Edgett, S., Kleinschmidt, E. Portfolio management for new product development: results of an industry practices study 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">Cooper, R.G., Edgett, S., Kleinschmidt, E. New Product Portfolio Management: Practices and Performance 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau">Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. Managing a portfolio of interdependent new product candidates in the pharmaceutical industry 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools for example a scoring model.

==Dsiclaimer for bias information==
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Talk:Bubble Diagram

2014-12-01T22:40:32Z

Tallimac: /* Comments from "Pppm student" */

== Article review by student: ==

*Nice to read, easy to follow structure
**'''''[Thank you!]'''''
*Nicely done with links to external wiki pages
**'''''[Thank you!]'''''

*In the bubble chart figure; what do the axis names/units correspond to (you mention parts in the text, but maybe you could add this to the chart)?
**'''''[fixed with a better description]'''''
* This sentence: “Although this tool excels in visualizing the portfolio, upstream processes are needs as comprehensive data analysis of all the dimensions of data.” I think it contains interesting information, but I don’t understand it.
**'''''[fixed]'''''
*Nice introduction to portfolio management and link to the model part, but you start out by describing portfolio management and end up talking about PPM. Maybe it might be sensitive to separate those more and put a greater focus on PPM, as this is what you continue with in the next chapter.
**'''''[valid point i get to the point much faster now]'''''
*The first few sentences of the ‘bubble chart/diagram’ repeat what you mention in the abstract. Might it be an idea to keep the abstract more generic and describe what it is used for rather then giving away to many details already?
**'''''[Hmm.. I like to be very precise in abstracts, but as it is a short article i understand it is annoying to the the same thing twice. 
IT has been done a bit more generic but not to much as it then looses it point as an abstract]'''''
*Would the illustration fit better into the method chapter? Than you could relate directly to it or maybe show other examples?
**'''''[illustration moved and table added]'''''
*This title: ” How can it be used and what are the effect (case study)” remember capital spelling (same for previous title) and effects got to be plural.
**'''''[true - was a draft - now corrected]'''''
*This sentence: “In this version the horizontal axis represents the total discounted value of the projects over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value).” It’s good that you explain NPVs, but what is discounted value?
**'''''[having changed it as I assume that everybody that understand NVP knows that discounted value is the same]'''''
*This chapter: “How can it be used and what are the effect (case study)” Would it make sense to merge it with your conclusion. Some elements state the same and you don’t really elaborate on the “how can it be used part”.
**'''''[True - The alternatives-discussion-conclusion have been changed]'''''

Good work! (or well on the way since you mention its not done:-))
 '''''[In general good feedback]'''''

== Comments from "Pppm student" ==

*The flow and the order of the sections in the article are good.
**'''''[Thank you!]'''''

*The text is easy to read.
**'''''[Thank you!]'''''

*Great with all the links in the article.
**'''''[Thank you!]'''''

*It is a little wired when you read exactly the same sentences again from the abstract in the start of the article. Maybe the abstract can be rewritten in some way with new formulations.
**'''''[true - rewritten to a bit more generic form and rephrasing ]'''''

*It would be nice with a better description of both the many versions of the BC and the different methods you mention in the article.
**'''''[True - i added a table to help with the understanding]'''''

**You can maybe use some more figures of the different versions to support the text in the section "Bubble Chart/Diagram".
**'''''[I would like to but can't see whih figures would support my text - table was added]'''''

*If it is possible to find a simple example of the Bubble Chart "in use", it could make the article even more interesting.
**'''''[I tried to bring in the pharmaceutical example, but the literature is vary limited]'''''

 '''''[Fine feedback - a bit scarce, but also a simple subject]'''''

Talk:Bubble Diagram

2014-12-01T22:40:24Z

Tallimac: /* Comments from "Pppm student" */

== Article review by student: ==

*Nice to read, easy to follow structure
**'''''[Thank you!]'''''
*Nicely done with links to external wiki pages
**'''''[Thank you!]'''''

*In the bubble chart figure; what do the axis names/units correspond to (you mention parts in the text, but maybe you could add this to the chart)?
**'''''[fixed with a better description]'''''
* This sentence: “Although this tool excels in visualizing the portfolio, upstream processes are needs as comprehensive data analysis of all the dimensions of data.” I think it contains interesting information, but I don’t understand it.
**'''''[fixed]'''''
*Nice introduction to portfolio management and link to the model part, but you start out by describing portfolio management and end up talking about PPM. Maybe it might be sensitive to separate those more and put a greater focus on PPM, as this is what you continue with in the next chapter.
**'''''[valid point i get to the point much faster now]'''''
*The first few sentences of the ‘bubble chart/diagram’ repeat what you mention in the abstract. Might it be an idea to keep the abstract more generic and describe what it is used for rather then giving away to many details already?
**'''''[Hmm.. I like to be very precise in abstracts, but as it is a short article i understand it is annoying to the the same thing twice. 
IT has been done a bit more generic but not to much as it then looses it point as an abstract]'''''
*Would the illustration fit better into the method chapter? Than you could relate directly to it or maybe show other examples?
**'''''[illustration moved and table added]'''''
*This title: ” How can it be used and what are the effect (case study)” remember capital spelling (same for previous title) and effects got to be plural.
**'''''[true - was a draft - now corrected]'''''
*This sentence: “In this version the horizontal axis represents the total discounted value of the projects over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value).” It’s good that you explain NPVs, but what is discounted value?
**'''''[having changed it as I assume that everybody that understand NVP knows that discounted value is the same]'''''
*This chapter: “How can it be used and what are the effect (case study)” Would it make sense to merge it with your conclusion. Some elements state the same and you don’t really elaborate on the “how can it be used part”.
**'''''[True - The alternatives-discussion-conclusion have been changed]'''''

Good work! (or well on the way since you mention its not done:-))
 '''''[In general good feedback]'''''

== Comments from "Pppm student" ==

*The flow and the order of the sections in the article are good.
**'''''[Thank you!]'''''

*The text is easy to read.
**'''''[Thank you!]'''''

*Great with all the links in the article.
**'''''[Thank you!]'''''

*It is a little wired when you read exactly the same sentences again from the abstract in the start of the article. Maybe the abstract can be rewritten in some way with new formulations.
**'''''[true - rewritten to a bit more generic form and rephrasing ]'''''

*It would be nice with a better description of both the many versions of the BC and the different methods you mention in the article.
**'''''[True - i added a table to help with the understanding]'''''

**You can maybe use some more figures of the different versions to support the text in the section "Bubble Chart/Diagram".
**'''''[I would like to but can't see whih figures would support my text - table was added]'''''

*If it is possible to find a simple example of the Bubble Chart "in use", it could make the article even more interesting.
**'''''[I tried to bring in the pharmaceutical example, but the literature is vary limited]'''''

 **'''''[Fine feedback - a bit scarce, but also a simple subject]'''''

Talk:Bubble Diagram

2014-12-01T22:39:28Z

Tallimac: /* Article review by student: */

Talk:Bubble Diagram

2014-12-01T22:35:35Z

Tallimac: /* Comments from "Pppm student" */

== Article review by student: ==

*Nice to read, easy to follow structure
**'''''[Thank you!]'''''
*Nicely done with links to external wiki pages
**'''''[Thank you!]'''''

*In the bubble chart figure; what do the axis names/units correspond to (you mention parts in the text, but maybe you could add this to the chart)?
**'''''[fixed with a better description]'''''
* This sentence: “Although this tool excels in visualizing the portfolio, upstream processes are needs as comprehensive data analysis of all the dimensions of data.” I think it contains interesting information, but I don’t understand it.
**'''''[fixed]'''''
*Nice introduction to portfolio management and link to the model part, but you start out by describing portfolio management and end up talking about PPM. Maybe it might be sensitive to separate those more and put a greater focus on PPM, as this is what you continue with in the next chapter.
**'''''[valid point i get to the point much faster now]'''''
*The first few sentences of the ‘bubble chart/diagram’ repeat what you mention in the abstract. Might it be an idea to keep the abstract more generic and describe what it is used for rather then giving away to many details already?
**'''''[Hmm.. I like to be very precise in abstracts, but as it is a short article i understand it is annoying to the the same thing twice. 
IT has been done a bit more generic but not to much as it then looses it point as an abstract]'''''
*Would the illustration fit better into the method chapter? Than you could relate directly to it or maybe show other examples?
**'''''[illustration moved and table added]'''''
*This title: ” How can it be used and what are the effect (case study)” remember capital spelling (same for previous title) and effects got to be plural.
**'''''[true - was a draft - now corrected]'''''
*This sentence: “In this version the horizontal axis represents the total discounted value of the projects over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value).” It’s good that you explain NPVs, but what is discounted value?
**'''''[having changed it as I assume that everybody that understand NVP knows that discounted value is the same]'''''
*This chapter: “How can it be used and what are the effect (case study)” Would it make sense to merge it with your conclusion. Some elements state the same and you don’t really elaborate on the “how can it be used part”.
**'''''[True - The alternatives-discussion-conclusion have been changed]'''''

Good work! (or well on the way since you mention its not done:-))
**'''''[In general good feedback]'''''

== Comments from "Pppm student" ==

*The flow and the order of the sections in the article are good.
**'''''[Thank you!]'''''

*The text is easy to read.
**'''''[Thank you!]'''''

*Great with all the links in the article.
**'''''[Thank you!]'''''

*It is a little wired when you read exactly the same sentences again from the abstract in the start of the article. Maybe the abstract can be rewritten in some way with new formulations.
**'''''[true - rewritten to a bit more generic form and rephrasing ]'''''

*It would be nice with a better description of both the many versions of the BC and the different methods you mention in the article.
**'''''[True - i added a table to help with the understanding]'''''

**You can maybe use some more figures of the different versions to support the text in the section "Bubble Chart/Diagram".
**'''''[I would like to but can't see whih figures would support my text - table was added]'''''

*If it is possible to find a simple example of the Bubble Chart "in use", it could make the article even more interesting.
**'''''[I tried to bring in the pharmaceutical example, but the literature is vary limited]'''''

Talk:Bubble Diagram

2014-12-01T22:32:30Z

Tallimac: /* Article review by student: */

== Article review by student: ==

*Nice to read, easy to follow structure
**'''''[Thank you!]'''''
*Nicely done with links to external wiki pages
**'''''[Thank you!]'''''

*In the bubble chart figure; what do the axis names/units correspond to (you mention parts in the text, but maybe you could add this to the chart)?
**'''''[fixed with a better description]'''''
* This sentence: “Although this tool excels in visualizing the portfolio, upstream processes are needs as comprehensive data analysis of all the dimensions of data.” I think it contains interesting information, but I don’t understand it.
**'''''[fixed]'''''
*Nice introduction to portfolio management and link to the model part, but you start out by describing portfolio management and end up talking about PPM. Maybe it might be sensitive to separate those more and put a greater focus on PPM, as this is what you continue with in the next chapter.
**'''''[valid point i get to the point much faster now]'''''
*The first few sentences of the ‘bubble chart/diagram’ repeat what you mention in the abstract. Might it be an idea to keep the abstract more generic and describe what it is used for rather then giving away to many details already?
**'''''[Hmm.. I like to be very precise in abstracts, but as it is a short article i understand it is annoying to the the same thing twice. 
IT has been done a bit more generic but not to much as it then looses it point as an abstract]'''''
*Would the illustration fit better into the method chapter? Than you could relate directly to it or maybe show other examples?
**'''''[illustration moved and table added]'''''
*This title: ” How can it be used and what are the effect (case study)” remember capital spelling (same for previous title) and effects got to be plural.
**'''''[true - was a draft - now corrected]'''''
*This sentence: “In this version the horizontal axis represents the total discounted value of the projects over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value).” It’s good that you explain NPVs, but what is discounted value?
**'''''[having changed it as I assume that everybody that understand NVP knows that discounted value is the same]'''''
*This chapter: “How can it be used and what are the effect (case study)” Would it make sense to merge it with your conclusion. Some elements state the same and you don’t really elaborate on the “how can it be used part”.
**'''''[True - The alternatives-discussion-conclusion have been changed]'''''

Good work! (or well on the way since you mention its not done:-))
**'''''[In general good feedback]'''''

== Comments from "Pppm student" ==

*The flow and the order of the sections in the article are good.

*The text is easy to read.

*Great with all the links in the article.

*It is a little wired when you read exactly the same sentences again from the abstract in the start of the article. Maybe the abstract can be rewritten in some way with new formulations.

*It would be nice with a better description of both the many versions of the BC and the different methods you mention in the article.

**You can maybe use some more figures of the different versions to support the text in the section "Bubble Chart/Diagram".

*If it is possible to find a simple example of the Bubble Chart "in use", it could make the article even more interesting.

Talk:Bubble Diagram

2014-12-01T22:26:22Z

Tallimac:

== Article review by student: ==

*Nice to read, easy to follow structure
*Nicely done with links to external wiki pages

*In the bubble chart figure; what do the axis names/units correspond to (you mention parts in the text, but maybe you could add this to the chart)?
* This sentence: “Although this tool excels in visualizing the portfolio, upstream processes are needs as comprehensive data analysis of all the dimensions of data.” I think it contains interesting information, but I don’t understand it.
*Nice introduction to portfolio management and link to the model part, but you start out by describing portfolio management and end up talking about PPM. Maybe it might be sensitive to separate those more and put a greater focus on PPM, as this is what you continue with in the next chapter.
*The first few sentences of the ‘bubble chart/diagram’ repeat what you mention in the abstract. Might it be an idea to keep the abstract more generic and describe what it is used for rather then giving away to many details already?
*Would the illustration fit better into the method chapter? Than you could relate directly to it or maybe show other examples?
*This title: ” How can it be used and what are the effect (case study)” remember capital spelling (same for previous title) and effects got to be plural.
*This sentence: “In this version the horizontal axis represents the total discounted value of the projects over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value).” It’s good that you explain NPVs, but what is discounted value?
*This chapter: “How can it be used and what are the effect (case study)” Would it make sense to merge it with your conclusion. Some elements state the same and you don’t really elaborate on the “how can it be used part”.

Good work! (or well on the way since you mention its not done:-))

== Comments from "Pppm student" ==

*The flow and the order of the sections in the article are good.

*The text is easy to read.

*Great with all the links in the article.

*It is a little wired when you read exactly the same sentences again from the abstract in the start of the article. Maybe the abstract can be rewritten in some way with new formulations.

*It would be nice with a better description of both the many versions of the BC and the different methods you mention in the article.

**You can maybe use some more figures of the different versions to support the text in the section "Bubble Chart/Diagram".

*If it is possible to find a simple example of the Bubble Chart "in use", it could make the article even more interesting.

Bubble Diagram

2014-12-01T22:25:10Z

Tallimac: /* Conclusion */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Cooper, R.G., Edgett, S., Kleinschmidt, E. 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau"> Managing a portfolio of interdependent new product candidates in the pharmaceutical industry Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools for example a scoring model.

==Dsiclaimer for bias information==
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:24:38Z

Tallimac: /* Dsiclaimer for bias information */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Cooper, R.G., Edgett, S., Kleinschmidt, E. 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau"> Managing a portfolio of interdependent new product candidates in the pharmaceutical industry Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. 
It should therefore never stand alone but synergies with other tools for example a scoring model.

==Dsiclaimer for bias information==
This articles is based upon s much scientific literature as possible. 
However much of the information comes from 7 articles published by the same authors: Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:23:17Z

Tallimac: /* Conclusion */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Cooper, R.G., Edgett, S., Kleinschmidt, E. 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau"> Managing a portfolio of interdependent new product candidates in the pharmaceutical industry Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. 
It should therefore never stand alone but synergies with other tools for example a scoring model.

==Dsiclaimer for bias information==
This articles is based upon s much scientific literature as possible. However much of the information comes from the 7 articles published by Cooper, R.G., Edgett, S., Kleinschmidt in the years 1997-2001.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:17:29Z

Tallimac:

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternative tools as the scoring model and [http://en.wikipedia.org/wiki/Decision_tree decision trees] is done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Cooper, R.G., Edgett, S., Kleinschmidt, E. 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau"> Managing a portfolio of interdependent new product candidates in the pharmaceutical industry Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. 
It should therefore never stand alone but synergies with other tools for example a scoring model.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:16:01Z

Tallimac: /* Conclusion */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Cooper, R.G., Edgett, S., Kleinschmidt, E. 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau"> Managing a portfolio of interdependent new product candidates in the pharmaceutical industry Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the benefits and disadvantages, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization it excel as one of the best but at the same time one can argue that the Bubble Chart is only as good as the information which is put into it. 
It should therefore never stand alone but synergies with other tools for example a scoring model.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:13:09Z

Tallimac:

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied <ref name="Cooper1999"></ref>. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Cooper, R.G., Edgett, S., Kleinschmidt, E. 16(4) p333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy <ref name="Class"></ref>.

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry <ref name="Blau"> Managing a portfolio of interdependent new product candidates in the pharmaceutical industry Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R. 31, p 227–245, 2004 </ref>. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:05:28Z

Tallimac:

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Cooper, R.G., Edgett, S., Kleinschmidt, E. 31(4), p 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long. It is not enough to execute the projects right, but you also have to execute the right projects at the right time <ref name="Class"></ref>.

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T22:00:21Z

Tallimac: /* References */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]][[Category:Complexity]]

Bubble Diagram

2014-12-01T21:59:20Z

Tallimac: /* Bubble Chart/Diagram */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. 
This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:58:50Z

Tallimac: /* Bubble Chart/Diagram */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable. 
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:58:02Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:57:34Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class"></ref>. The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:56:57Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison . The problem with this method is that the score itself does not tell why it has the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:55:50Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class">. The problem with this method is that the score itself does not tell why it hs the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:54:01Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class">. The problem with this method is that the score itself does not tell why it hs the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros & cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<ref>
<references/>

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:53:36Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class">. The problem with this method is that the score itself does not tell why it hs the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<refs>
<references/>

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:53:05Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class">. The problem with this method is that the score itself does not tell why it hs the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references/>

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:52:22Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It simple exist of for example 20 questions/criteria that should be ranked from 1-5 on how well they fit. This will hereafter yield the project score that can be used for comparison <ref name="Class">. The problem with this method is that the score itself does not tell why it hs the score that is has. So after looking at the project score, the user only have a vague idea of why this project is good. The scoring model should not be used as a alternative but rather with the BC to get the best possible data. 
Another way of doing project prioritization is using the The Decision tree approach. This has also been a fairly used approach as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:35:00Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Is is a bit hard to substitute a chart, bubble or regular, as it is one of the most dominate ways to visual data.
One alternative could be using a scoring model. Many different scoring models have been developed to fit the specific company. 
In comparison to the BC this method can take many aspects into consideration. It can often and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:16:22Z

Tallimac: /* The use of a Bubble Chart */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation of such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:10:59Z

Tallimac: /* Bubble Chart/Diagram */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. 
As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:10:31Z

Tallimac: /* Bubble Chart/Diagram */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
The origin of the BC is unknown, and might be due to the fact that it is a expansion of the regular xy-scatter-plot. It should be mentioned that this tool shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio</ref>. 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of difference between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T21:01:33Z

Tallimac: /* Bubble Chart/Diagram */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards? The answer depends on the company and the values and beliefs therein. The BC does not give the answer but makes the overview much more clear and balancing the portfolio with this visual tool might be a more manageable.
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T20:35:21Z

Tallimac: /* Introduction to Portfolio Management */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T20:34:25Z

Tallimac: /* Introduction to Portfolio Management */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that a company that does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capabilities to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T19:29:16Z

Tallimac:

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done.

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:34:37Z

Tallimac: /* Alternatives */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:27:09Z

Tallimac: /* Disadvantages */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. 
This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. 
The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many alternatives exist. In the former section it was clearly seen that a nonlinear programming yielded a solution 28 % better than the BC.
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:26:22Z

Tallimac: /* Disadvantages */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. This for example mean that the risk of every project should be calculated to have a number between 1 and 100. 
Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data. 
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, 
but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many alternatives exist. In the former section it was clearly seen that a nonlinear programming yielded a solution 28 % better than the BC.
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:25:25Z

Tallimac: /* Benefits */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. 
Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. 
This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. 
The amount of time spent on reading long qualitative report could be cut significantly. 
This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. This for example mean that the risk of every project should be calculated to have a number between 1 and 100. Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data.
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many alternatives exist. In the former section it was clearly seen that a nonlinear programming yielded a solution 28 % better than the BC.
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:24:54Z

Tallimac: /* Benefits */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. The amount of time spent on reading long qualitative report could be cut significantly. This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. This for example mean that the risk of every project should be calculated to have a number between 1 and 100. Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data.
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many alternatives exist. In the former section it was clearly seen that a nonlinear programming yielded a solution 28 % better than the BC.
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:24:16Z

Tallimac: /* Conclusion */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. The amount of time spent on reading long qualitative report could be cut significantly. This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. This for example mean that the risk of every project should be calculated to have a number between 1 and 100. Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data.
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many alternatives exist. In the former section it was clearly seen that a nonlinear programming yielded a solution 28 % better than the BC.
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Looking at the pros&cons, it is evident that if the upstream processes already exist, this tool will add value to the user.
The BC should be continuously used as a tool to get clearer overview, and to help managers balance the portfolio.
It is very important to understand the limitations of this tool as the comparability of different types of projects can be very low. At the same time it cannot show dependencies and is not a dynamic tool, but a "snapshot overview" of the information that is available today. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it. It should therefore never stand alone but synergies with other tools.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]

Bubble Diagram

2014-12-01T17:17:48Z

Tallimac: /* Conclusion */

The [http://en.wikipedia.org/wiki/Bubble_chart Bubble Chart] is used as a visual tool to support the decision making in [http://en.wikipedia.org/wiki/Project_portfolio_management Project Portfolio Management]. The chart is much similar to the regular xy-[http://en.wikipedia.org/wiki/Scatter_plot scatter-plot], however including the variables size and colour, the user can get up to four dimensions of data in an easily understandable two-dimensional chart. In the most popular version the horizontal axis represents the [http://en.wikipedia.org/wiki/Net_present_value Net Present Value] over a period of time and the vertical axis represents the probability of success.
A Study has shown that more than 40 % of examined companies use this method, although only 5-8 % use it as the dominant tool. <ref name="Class"> Portfolio management for new product development: results of an industry practices study Volume 31, Issue 4, pages 361–380, October 2001 </ref> If this picture is representative is however not evident. The article aims to provide a clear overview trough a description of the tool and its uses in practice and further raises the question if a tool as simplistic as the bubble chart can add real support to the decision making in Project Portfolio Management?
A short comparison of the Bubble Chart to alternatives tools such as [http://en.wikipedia.org/wiki/Decision_tree decision trees] and scoring models are done

=Introduction to Portfolio Management=
In order to obtain a profitable business in the 21th century, an organisation must excel in almost every way possible, from long-term [http://en.wikipedia.org/wiki/Strategic_management strategic management] to everyday [http://en.wikipedia.org/wiki/Automated_planning_and_scheduling planning and scheduling]. Constant increasing global competition, ever faster changing technologies together with shorter [http://en.wikipedia.org/wiki/Product_lifecycle life cycles] make rivalry even harder today than ever. It is evident that if a company does not master [http://en.wikipedia.org/wiki/Project_management project management] and does not have the capability to execute projects swift and efficiently, will not be able to maintain a profitable business for long.
It is not enough to execute the projects right, but you also have to execute the right projects at the right time. <ref name="Weird">Portfolio Management: Fundamental for New Product Success ???</ref>,

In order to manage all these parameter and always be one step ahead of competitors, few errors can be made. One way to make sure this happens is to make use of Portfolio Management. There are many aspects to Portfolio management and many definitions can be found in the literature. The benefits of Portfolio Management are also somewhat dependent on in which area of business it is applied [REF]. This article will be scoped around Project Portfolio Management (PPM).
Cooper et al. (1999) argues that from a company perspective PPM is all about how to invest money in order to make more money <ref name="Cooper1999">New Product Portfolio Management: Practices and Performance Volume 16, Issue 4, pages 333–351, July 1999</ref>, and such optimization is sometimes also referred to as Project Portfolio Optimization (PPO). Choosing the right projects and building the best portfolio is however much more complicated than selecting the most profitable projects, based on a simple cost-benefit analysis, as the most profitable projects do not necessarily create the most profitable business. PPM is about prioritizing some projects over others to balance the portfolio in regards to for example risk and/or resource allocation. This makes sure that the portfolio is in balance with the values and believes of the company as well as fits the current and future market.

One of the most important trades of PPM is the assistance to the decision makers in making the right strategic choices. It is of greatest importance that all projects, small as big, are aligned internally and do not counter work, but instead support the overall corporate strategy [REF].

=Bubble Chart/Diagram=
[[File:Bubble_chart.png|360px|thumb|right|A Bubble Chart with its classic 4 quadrants. The x-axis is NPV and the y-axis is the probability of success. This means that pearls are always preferred, but usually, there are not enough resources to invest in both "Bread & Butter" and "Oyster" projects and trade-offs have to be done.]]
The Bubble Chart (BC) is one of the tools that can be used to supports the decision making in PPM.
The BC is a two-dimensional chart where bubbles/disks are plotted. Much similar to the regular xy-scatter-plot, but instead of dots data is plotted as bubbles/disks. In addition the size of bubble, the color can also vary giving the user up to four dimensions of data in an easily understandable two-dimensional chart. Cooper et al. (2001) reports that BCs are quite abundant with approximately 41 % of companies using this method in some form or another. Only 5-8 % of the companies however use it as their dominant method <ref name="Class"></ref>. Due to the simplicity of the chart, many versions exist and are used extensively. However one version shows to be very dominant as almost 45 % of all BC-users use this design<ref name="Class"></ref>.
In this version the horizontal axis represents the total discounted value of the given project over a period of time (often 5-20 years) and is often referred to as NPV (Net Present Value). The vertical axis represents the probability of success, either as the probability in regards to the risk involved or the probability regarding the technical success (see the figure to the right). Lastly the size of the bubble represents cost, and thereby giving a very intuitive feeling of bubbles that are big will also represent big and demanding projects. It is important to notice that since the area of a circle grows with the quadrant of the radius, the cost should not be linear to the radius, but to the total size of the circle. 
This chart can be divided into 4 quadrants: low-low, low-high, high-low and high-high. It is obvious that projects located within the high-high area are often desired and opposite projects that are located within the low-low quadrant are unwanted.
The more complicated situation arrives when projects prioritization needs to be done in either the low-high and high-low area. Trade-offs is necessary but what is more attractive? Is it a high chance of success with small rewards or the low chance of success with high rewards?...MÅSKE SVAR 
It should be mentioned that this method shares some similarity to the Boston Matrix made in 1970 by Bruce D. Henderson which also works as a tool to assist in portfolio decisions [REF]. <ref name="BCG"> https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio </ref>...HVAD MERE 
As the color of the bubbles also serves as a dimension of data, it is therefore possible to distinct certain projects from each other no matter where they are located in the matrix. This can be very important if it some kind of dependency between projects should be visualized. As stated, nearly 45 % of companies that uses Bubble Charts uses a dominant design. Below a table can be found that shows the result of a performed study<ref name="Class"></ref>.

{|class="wikitable" style="margin: auto;"
|'''Chart Type'''
|'''X-Axis'''
|'''Y-Axis'''
|'''%'''
|-
|Risk vs. Reward
|NPV, Total benefit after years of launch
|Probability of Succes
|44,4
|-
|Newness
|Technical newness
|Market newness
|11,1
|-
|Ease Vs. Attractiveness
|Technical feasibility
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Strengths Vs. Attractiveness
|Competitive position
|Market attractiveness (growth, life cycle length etc.)
|11,1
|-
|Cost Vs. timing
|Cost to implement
|Time to impact
|9,7
|-
|Strategic Vs. Benefit
|Strategic focus or fit
|Business intent, NPV, attractiveness
|8,9
|-
|Cost Vs. benefit
|Cumulative Reward
|Cumulative development costs
|5,6
|}
 

=The use of a Bubble Chart=
The Bubble Chart is a simple tool that is meant to fairly quick and easy help managers get an overview and make fast decisions. However as this is a tool with up to 4 dimensions of data, it relies on some kind of source feeding it information. In other words, The BC cannot be made before the right upstream processes are in place. Very few or no studies can be found in the literature regarding the exact effect of the chart in use. It is therefore impossible to generalize and conclude generically whether the BC is worth implementing in a company. In order to get an overview a list of benefits and disadvantages is conducted. This list can be used to understand in which situation BCs would be easy to implement and use, and in which situation the implementation such a chart can be a costly and non-benefiting project.

===Benefits===
<ol start="B1">
<li>'''Easy implementation'''</li>
If proper upstream processes are in place, this tool can be implemented very easy. Depending on which software is used, it might not take more than a single "create chart" macro and the bubble chart should be ready to print. This could be done by low-paid employee as student workers.
<li>'''Intuitive and no training needed'''</li>
As this tool is so simple, the amount of training needed is close to zero. This is good as the cost of employee training can posses a great expenses.
<li>'''Easy comparability between alike Projects'''</li>
The fact that the tool is focusing on delivering visual data, decision makers can get fast overview maybe enough data to make quick decisions. The amount of time spent on reading long qualitative report could be cut significantly. This said, the bubble chart should maybe not be the only source of data for decisions making.
<li>'''Can be low maintenance'''</li>
As this tool just visualize already existing information, the only maintenance is updating the chart and print again.
</ol>

===Disadvantages===
<ol start="1">
<li>'''Hard to quantify data correctly'''</li>
In order to use data for the BC, it is important that everything is quantified. This for example mean that the risk of every project should be calculated to have a number between 1 and 100. Doing this can be very hard and some projects might be almost impossible to quantify.
<li>'''Relies on upstream processes'''</li>
All mistakes that are done, and all miscalculation that have been performed will show in the BC as it is relies directly on this data.
This makes the "system" more fragile due to the fact that one mistakes will show up several times.
<li>'''No comparability between different types of projects will often neglect not-financial driven projects'''</li>
It is always hard to compare different types of project. Doing strategic projects that might help the company solve different future problems can be hard to compare with a product-development project. Both types of project are important and can be of great value, but the NPV of strategic fitting projects is hard to know, and the product-development project will often seem more popular.
<li>'''Static model that cannot show project synergy'''</li>
It might be that two projects have several steps in common, and by doing project a, the cost of project b will be lowered by 25 % and vice versa. The fact that the chart cannot show this kind of dependency can make the tool very less usable if a larger number of projects with great dependencies need to be invested in.
</ol>

==Discussion==
The axes and therefore the use of the BCs can vary a lot as seen in the table in the previous section. But the level of detail can also vary. If the axes on the BC are not defined in numbers, but rather just called low-high, the requirements for upstream processes have changed. Instead of having any calculation, a little portfolio board with experience would be able to make a BC rather fast based on gut-feeling and experience from earlier projects.

Blau et al. (2004) examines the effect of a Bubble Chart compared to a computational decision support system in the pharmaceutical industry. Even though a setup of highly inter-dependent data was used, the multi-criteria genetic algorithm only yielded a result 28 % better (with the same level of risk) than with the conventional bubble chart approach. This emphasizes two important points:
<ol start="1">
<li>The bigger the investment, the more data-analysis should be made in order to not loose millions</li>
<li>The bubble chart approach gives an easy overview and serves its purpose very well in the early stages to assist managers in decision making regarding Portfolio management of any kind.</li>
</ol>
It can be discussed if a margin of 28 % is a lot or not. Looking at the the result in practice, the 28 % margin in the pharmaceutical industry can easily mean the difference of a billion dollars. Looking at it from the opposite perspective, a margin of 28 % is not that big, if it is taken into consideration that this is the difference between the most complicated and the most simple method. The result in itself is also an estimate, as forecasts rarely fit perfectly.

=Alternatives=
Many alternatives exist. In the former section it was clearly seen that a nonlinear programming yielded a solution 28 % better than the BC.
Many different scoring techniques have also been developed. In comparison to the BC this method can take many aspects into consideration, and depending in the score can go from simple to comprehensive.
The Decision tree approach has also been fairly used as it gives the user the ability to get a simple overview. However this method has received criticism due to the fact that the tree can quickly grow to a size where the simple overview is lost and the methods therefore uses one of it greatest benefits.

=Conclusion=
Oven though The BC should be continuously used as a tool to get clearer overview, but it is important to understand the limitations of this tool. As it is a tool for visualization one can argue that the Bubble Chart is only as good as the information which is put into it.
This combined gives the user the opportunity to observe four-dimensional information in a very easy and simple two-dimensional chart. This feature is what makes the BC different from many other tools as it provides the user much data in a fast and visual manner.

the chart is used in an early stage to get an easy overview.

=References=
<references />

[[Category:Portfolio Management]][[Category:Visual tools]][[Category:Project prioritization]]