Concept of Risk Quantification and Methods used in Project Management
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===Importance=== | ===Importance=== | ||
+ | <div style="text-align: justify;">The term risk or risk assessment may sound like a modern scientific concept, but the idea of risk is as old as recorded human history. The gambling, the very essence of risk, was a popular pastime that inspired Pascal and Fermat’s revolutionary breakthrough into laws of probability <ref>[''Bernstein P.L., “Against the Gods: The remarkable story of risk”, John Wiley & Sons, New York, (1996).''] </ref>. However, Risk as a scientific field is quite young. Around 30-40 years ago scientific journals, papers, and conferences started to cover this idea and principles on how to assess and manage risk <ref>[''Aven T., “Risk assessment and risk management: Review of recent advances on their foundation”, European journal of operational research, (2016), Vol. 253, No. 1, pp. 1-13.'']</ref>. One of the main reasons of project failures is inadequate risk management. Figure 2 shows that 17% of projects fail due to inadequate risk management. Moreover, according to Standish Group (2013)<ref>[Standish. THE CHAOS MANIFESTO. Standish Group, Boston (2013).]</ref>, 59% of IT projects overrun by original cost estimate and 74% are overrun by original time estimate. In software or IT projects, a number of factors contribute to the uncertain outcome of a project. Nogueira et al. (2014)<ref>[Nogueira, Marcelo, and Ricardo J. Machado. “Importance of Risk Process in Management Software Projects in Small Companies.” Ifip Advances in Information and Communication Technology, Vol. 439, No. 2, (2014), pp. 358–365. Web.]</ref> concluded that when a scope is defined and software production teams are guided through the risk process then it becomes easier to take a rational decision. Present decisions may result in future losses or gains. If there is no risk assessment then banks will not be able to make decisions on which projects to finance and which not<ref>[Bernadete Junkes, M., Anabela P. Tereso, and Paulo S. L. P. Afonso. “The Importance of Risk Assessment in the Context of Investment Project Management: a Case Study.” Procedia Computer Science 64 (2015): pp. 902–910. Web.]</ref>. Many construction projects fail to achieve their time, cost and quality goals due to several unforeseeable uncertain events like weather conditions, subcontractor failure, or different site conditions<ref>[Mustafa, Mohammad A., and Jamal F. Al-Bahar. “Project Risk Assessment Using the Analytic Hierarchy Process.” Ieee Transactions on Engineering Management, Vol. 38, No.1, (1991), pp. 48-50. Print.]</ref>. Comprehensive risk assessment can help an organization to quantify risks and prepare contingencies beforehand so that projects can be completed in their original time, cost, and quality estimates. | ||
+ | This implies that the importance of risk assessment cannot be overlooked. First, risk quantification help in preparing contingencies for time and cost estimates. Second, It helps organizations in taking a rational decision in the presence of uncertainty. And third, it provides confidence of dealing unforeseeable events in future rather than acting irrationally. | ||
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[[File:Why projects fail.png|thumb| |upright=2.5||center||Figure 2: Causes of project failure (source: https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAg4AAAAJDVlMzhiNDM5LWJlMWUtNGU5Zi05ZTY4LTAzYWRhODM5YjhmYQ.png)]] | [[File:Why projects fail.png|thumb| |upright=2.5||center||Figure 2: Causes of project failure (source: https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAg4AAAAJDVlMzhiNDM5LWJlMWUtNGU5Zi05ZTY4LTAzYWRhODM5YjhmYQ.png)]] | ||
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=Analysis of Risk Management Principles and Processes= | =Analysis of Risk Management Principles and Processes= | ||
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[[File:Example of Monte Carlo simulation run.png |thumb| |upright=2.5||center||Figure 7: Example of Monte Carlo simulation run(source: http://quantmleap.com/blog/2010/07/project-risk-management-and-the-application-of-monte-carlo-simulation/)]] | [[File:Example of Monte Carlo simulation run.png |thumb| |upright=2.5||center||Figure 7: Example of Monte Carlo simulation run(source: http://quantmleap.com/blog/2010/07/project-risk-management-and-the-application-of-monte-carlo-simulation/)]] | ||
====Monte Carlo Analysis or Simulation==== | ====Monte Carlo Analysis or Simulation==== | ||
− | <div style="text-align: justify;">Monte Carlo is a computerized mathematical simulation technique that is used to quantify risks in project management. This technique is helpful in seeing the probable outcomes of decisions and assesses the impact of risk that is useful in decision making [http://www.palisade.com/risk/monte_carlo_simulation.asp]. Most likely and least likely estimates of risks are provided for each event and then these estimates are summed together to calculate a range of possible outcome. Monte Carlo simulation then generates random values between the range and calculates the number of occurrences the value lies within each possible outcome. This probability is then distributed and the decision is made based on the most probable outcome. | + | <div style="text-align: justify;">Monte Carlo is a computerized mathematical simulation technique that is used to quantify risks in project management. This technique is helpful in seeing the probable outcomes of decisions and assesses the impact of risk that is useful in decision making [http://www.palisade.com/risk/monte_carlo_simulation.asp]. Most likely and least likely estimates of risks are provided for each event and then these estimates are summed together to calculate a range of possible outcome. Monte Carlo simulation then generates random values between the range and calculates the number of occurrences the value lies within each possible outcome<ref>[Ahmed, A. et al. (2003a), “A conceptual framework for risk analysis in concurrent engineering”, (R1.6 Paper No. 86), Proceedings of the 17th International Conference on Production |
+ | Research, 4-7 August, Blacksburg, Virginia, USA.]</ref>. This probability is then distributed and the decision is made based on the most probable outcome. | ||
For example, if there are three tasks required in an e-learning project. Best case, most likely, and worst case estimates of all the tasks required are given in figure 5. It can be seen that the project is most likely to complete in between 11 and 23 days. Now for example, if Monte Carlo simulation is run 500 times generating random values between 11 and 23. The total number of times the simulation result was less than or equal to projected duration is calculated as shown in figure 6. Then, the probability of each projected duration is calculated and distributed as shown in figure 7. It can be seen that, from figure 5, the most likely projected completion time is 17 days. But, as per figure 7, Monte Carlo simulation shows that likelihood of project completion in 17 days is almost 33%. Whereas, the likelihood of project completion in 19 days is 88%. Hence, it can be estimated that the project will most likely complete in 19 to 20 days. [http://quantmleap.com/blog/2010/07/project-risk-management-and-the-application-of-monte-carlo-simulation/] | For example, if there are three tasks required in an e-learning project. Best case, most likely, and worst case estimates of all the tasks required are given in figure 5. It can be seen that the project is most likely to complete in between 11 and 23 days. Now for example, if Monte Carlo simulation is run 500 times generating random values between 11 and 23. The total number of times the simulation result was less than or equal to projected duration is calculated as shown in figure 6. Then, the probability of each projected duration is calculated and distributed as shown in figure 7. It can be seen that, from figure 5, the most likely projected completion time is 17 days. But, as per figure 7, Monte Carlo simulation shows that likelihood of project completion in 17 days is almost 33%. Whereas, the likelihood of project completion in 19 days is 88%. Hence, it can be estimated that the project will most likely complete in 19 to 20 days. [http://quantmleap.com/blog/2010/07/project-risk-management-and-the-application-of-monte-carlo-simulation/] | ||
Monte Carlo simulation is usually used in cost and schedule estimation. It can also be used in large projects or programs. The benefits of using Monte Carlo are easiness of tool, numerical estimation, and greate level of confidence [http://quantmleap.com/blog/2010/07/project-risk-management-and-the-application-of-monte-carlo-simulation/]. Whereas drawbacks or challenges are the use of right distribution as wrong distribution may lead to wrong results, input estimates as right estimates are required to produce right results, and use of right mathematical formula in the software.[http://abovethelaw.com/2016/05/finance-and-law-the-pros-and-cons-of-monte-carlo-simulations-in-valuation/] | Monte Carlo simulation is usually used in cost and schedule estimation. It can also be used in large projects or programs. The benefits of using Monte Carlo are easiness of tool, numerical estimation, and greate level of confidence [http://quantmleap.com/blog/2010/07/project-risk-management-and-the-application-of-monte-carlo-simulation/]. Whereas drawbacks or challenges are the use of right distribution as wrong distribution may lead to wrong results, input estimates as right estimates are required to produce right results, and use of right mathematical formula in the software.[http://abovethelaw.com/2016/05/finance-and-law-the-pros-and-cons-of-monte-carlo-simulations-in-valuation/] | ||
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====Decision Trees==== | ====Decision Trees==== | ||
[[File:Example of decision tree.png |thumb| |upright=2.5||center||Figure 9: Example of decision tree analysis (source: https://www.mpug.com/articles/pmp-prep-decision-tree-analysis-in-risk-management/)]] | [[File:Example of decision tree.png |thumb| |upright=2.5||center||Figure 9: Example of decision tree analysis (source: https://www.mpug.com/articles/pmp-prep-decision-tree-analysis-in-risk-management/)]] | ||
− | <div style="text-align: justify;">Decision tree is a tool that uses tree-like graph or model of decisions and their corresponding consequences [http://en.wikipedia.org/wiki/Decision_tree] that can be used to quantify risks and make a decision under uncertainty in a project. Expected Monetary Value (EMV) is usually used to quantify risks, where probability(P) of an event is multiplied by its impact(I) to calculate the EMV. For example, if there is a decision to make in a project under uncertainty that whether make a prototype or not in a project. This decision has only two options, prototype and no prototype, shown in figure 9. Each of these choices has two consequences, success or failure. The probability of each consequence is also shown in figure 9. Impact in terms of costs for each option or chance and consequence or outcome is also shown in figure 9. Net path value for prototype with 70% success is equal to payoff minus prototype cost i.e. $500,000 - $100,000 = +$400,000. Similarly, net path values for rest of the paths are also shown in figure 9. EMV value for the path option of prototype is then calculated as [70%*($400,000) + 30%*(-$150,000)] = +$235,000. Similarly, the EMV value for no prototype is -$100,000. Hence, EMV value at decision node will be +$235,000, which means that the project manager should decide to select prototype option as the other option actually gives a loss. [http://www.mpug.com/articles/pmp-prep-decision-tree-analysis-in-risk-management/] | + | <div style="text-align: justify;">Decision tree is a tool that uses tree-like graph or model of decisions and their corresponding consequences [http://en.wikipedia.org/wiki/Decision_tree] that can be used to quantify risks and make a decision under uncertainty in a project. Expected Monetary Value (EMV) is usually used to quantify risks, where probability(P) of an event is multiplied by its impact(I) to calculate the EMV<ref>[Clemen, R.T., Making Hard Decisions: An Introduction to Decision Analysis, Druxbury Press, New York, NY. (1996).]</ref><ref>[Russell, R.S. and Taylor, B.W. III, Operations Management, Prentice-Hall Inc., Upper Saddle River, NJ, (2000).]</ref><ref>[Clemen, R.T. and Reilly, T., Making Hard Decisions with Decision Tools, Druxbury Thomson Learning, Toronto, (2001).]</ref><ref>[Perry, J.G. and Haynes, R.W., “Risk and its management in construction projects”, Proceedings of Institution of Civil Engineers, (1985), pp. 499-521.]</ref><ref>[Ahmed, Ammar, Berman Kayis, and Sataporn Amornsawadwatana. “A Review of Techniques for Risk Management in Projects.” Ed. by S.C.L. Koh. Benchmarking, Vol. 14, No.1, (2007), pp. 22–36. Web.]</ref>. For example, if there is a decision to make in a project under uncertainty that whether make a prototype or not in a project. This decision has only two options, prototype and no prototype, shown in figure 9. Each of these choices has two consequences, success or failure. The probability of each consequence is also shown in figure 9. Impact in terms of costs for each option or chance and consequence or outcome is also shown in figure 9. Net path value for prototype with 70% success is equal to payoff minus prototype cost i.e. $500,000 - $100,000 = +$400,000. Similarly, net path values for rest of the paths are also shown in figure 9. EMV value for the path option of prototype is then calculated as [70%*($400,000) + 30%*(-$150,000)] = +$235,000. Similarly, the EMV value for no prototype is -$100,000. Hence, EMV value at decision node will be +$235,000, which means that the project manager should decide to select prototype option as the other option actually gives a loss. [http://www.mpug.com/articles/pmp-prep-decision-tree-analysis-in-risk-management/] |
Benefits of using decision tree analysis are ease of understanding and implementation, quantification of even little hard data, and a possibility to add several new scenarios. While disadvantages are biases of input data and increase in complexity for a large number of outcomes that are linked together. [http://en.wikipedia.org/wiki/Decision_tree] </div><br /> | Benefits of using decision tree analysis are ease of understanding and implementation, quantification of even little hard data, and a possibility to add several new scenarios. While disadvantages are biases of input data and increase in complexity for a large number of outcomes that are linked together. [http://en.wikipedia.org/wiki/Decision_tree] </div><br /> | ||
Revision as of 14:08, 30 September 2017
Contents |
Introduction
Definition
Inputs and Outputs of Risk Quantification
In risk quantification process of a project, there are inputs that should be considered with delegate care and as a result of risk quantification process outputs are generated. According to PMBOK, following inputs are considered and outputs are produced in risk quantification process of any project:
Inputs | Outputs |
---|---|
Stakeholder Risk Tolerance: Every organization and different individuals may have different tolerance for risk value | Opportunities to Pursue, Threats to Respond to: The list of opportunities that should be pursued and threats that should be taken care of. |
Sources of Risks: Categories of possible risk events that may negatively affect the outcome of a project. For example, Designs errors, stakeholder actions, or poor estimates etc. | Opportunities to Ignore, Threats to Accept: List of opportunities that can be ignored and threats that can be accepted. |
Potential Risk Events: Discrete occurrences that can occur during a project that may affect the outcome of the project. Such as natural disaster or departure of key member etc. | |
Cost Estimates: Assessment of likely cost required to complete the project activities. | |
Activity Duration Estimate: Quantitative assessment of likely number of work period required to activities of a project |
Purpose and Concept
Importance
This implies that the importance of risk assessment cannot be overlooked. First, risk quantification help in preparing contingencies for time and cost estimates. Second, It helps organizations in taking a rational decision in the presence of uncertainty. And third, it provides confidence of dealing unforeseeable events in future rather than acting irrationally.
Analysis of Risk Management Principles and Processes
Applications
Methods
Expert Opinion
Expected Monetary Value (EMV)
Statistical Sums
Monte Carlo Analysis or Simulation
For example, if there are three tasks required in an e-learning project. Best case, most likely, and worst case estimates of all the tasks required are given in figure 5. It can be seen that the project is most likely to complete in between 11 and 23 days. Now for example, if Monte Carlo simulation is run 500 times generating random values between 11 and 23. The total number of times the simulation result was less than or equal to projected duration is calculated as shown in figure 6. Then, the probability of each projected duration is calculated and distributed as shown in figure 7. It can be seen that, from figure 5, the most likely projected completion time is 17 days. But, as per figure 7, Monte Carlo simulation shows that likelihood of project completion in 17 days is almost 33%. Whereas, the likelihood of project completion in 19 days is 88%. Hence, it can be estimated that the project will most likely complete in 19 to 20 days. [10] Monte Carlo simulation is usually used in cost and schedule estimation. It can also be used in large projects or programs. The benefits of using Monte Carlo are easiness of tool, numerical estimation, and greate level of confidence [11]. Whereas drawbacks or challenges are the use of right distribution as wrong distribution may lead to wrong results, input estimates as right estimates are required to produce right results, and use of right mathematical formula in the software.[12]
Decision Trees
Selection of Technique
- Resources and capabilities required to execute a certain risk quantification method
- Degree of uncertainty in the project
- Complexity of the project
- Availability of the past data
Table 3 shows a framework for selecting the right method based on the nature of the project. (This framework provides author’s subjective analysis and hence prone to disagreement.)
Limitations and Challenges
All these facts, make one questions that when risk assessment or quantification cannot guarantee the success of a project then why do managers invest so much effort and money into risk assessment. The answer lies in a famous phrase “better than nothing”. It is always better to perform risk assessment beforehand and be prepared and control for uncertain events than drastically act on uncertain events unprepared when they occur. [further writing in process....]
Annotated Bibliography
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 [Duncan W. R., “A Guide to Project Management Body of Knowledge (PMBOK)”, PMI Standards Committee, (2013).]
- ↑ [Bernstein P.L., “Against the Gods: The remarkable story of risk”, John Wiley & Sons, New York, (1996).]
- ↑ [Aven T., “Risk assessment and risk management: Review of recent advances on their foundation”, European journal of operational research, (2016), Vol. 253, No. 1, pp. 1-13.]
- ↑ [Standish. THE CHAOS MANIFESTO. Standish Group, Boston (2013).]
- ↑ [Nogueira, Marcelo, and Ricardo J. Machado. “Importance of Risk Process in Management Software Projects in Small Companies.” Ifip Advances in Information and Communication Technology, Vol. 439, No. 2, (2014), pp. 358–365. Web.]
- ↑ [Bernadete Junkes, M., Anabela P. Tereso, and Paulo S. L. P. Afonso. “The Importance of Risk Assessment in the Context of Investment Project Management: a Case Study.” Procedia Computer Science 64 (2015): pp. 902–910. Web.]
- ↑ [Mustafa, Mohammad A., and Jamal F. Al-Bahar. “Project Risk Assessment Using the Analytic Hierarchy Process.” Ieee Transactions on Engineering Management, Vol. 38, No.1, (1991), pp. 48-50. Print.]
- ↑ 8.0 8.1 8.2 8.3 [ISO 31000: Risk Management - Principles and Guidelines. (2009).]
- ↑ 9.0 9.1 9.2 [PRINCE2: A Practical Handbook, PRINCE2. (2009). Prince2: a Practical Handbook. Butterworth-Heinemann.]
- ↑ [Yildiz A. Z. et al, “Using expert opinion for risk assessment: a case study of a construction project utilizing a risk mapping tool “, Procedia - Social and Behavioral Sciences, (2014), Vol. 119, pp. 519-528.]
- ↑ [Ahmed, A. et al. (2003a), “A conceptual framework for risk analysis in concurrent engineering”, (R1.6 Paper No. 86), Proceedings of the 17th International Conference on Production Research, 4-7 August, Blacksburg, Virginia, USA.]
- ↑ [Clemen, R.T., Making Hard Decisions: An Introduction to Decision Analysis, Druxbury Press, New York, NY. (1996).]
- ↑ [Russell, R.S. and Taylor, B.W. III, Operations Management, Prentice-Hall Inc., Upper Saddle River, NJ, (2000).]
- ↑ [Clemen, R.T. and Reilly, T., Making Hard Decisions with Decision Tools, Druxbury Thomson Learning, Toronto, (2001).]
- ↑ [Perry, J.G. and Haynes, R.W., “Risk and its management in construction projects”, Proceedings of Institution of Civil Engineers, (1985), pp. 499-521.]
- ↑ [Ahmed, Ammar, Berman Kayis, and Sataporn Amornsawadwatana. “A Review of Techniques for Risk Management in Projects.” Ed. by S.C.L. Koh. Benchmarking, Vol. 14, No.1, (2007), pp. 22–36. Web.]