Bubble Diagram

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Developed by Peter Busk

The Bubble Chart is used as a visual tool to support the decision making in Project Portfolio Management. The chart is much similar to the regular xy-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 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. [1] 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 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 strategic management to everyday planning and scheduling. Constant increasing global competition, ever faster changing technologies together with shorter life cycles make rivalry even harder today than ever. It is evident that a company that does not master 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 [1].

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 [2]. 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 [2], 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 [1].

Bubble Chart/Diagram

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 [1]. 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[1]. 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 [3].
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[1].

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.


  1. Easy implementation
  2. 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.

  3. Intuitive and no training needed
  4. 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.

  5. Easy comparability between alike Projects
  6. 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.

  7. Can be low maintenance
  8. As this tool just visualize already existing information, the only maintenance is updating the chart and print again.


  1. Hard to quantify data correctly
  2. 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.

  3. Relies on upstream processes
  4. 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.

  5. No comparability between different types of projects will often neglect not-financial driven projects
  6. 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.

  7. Static model that cannot show project synergy
  8. 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.


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 [4]. 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:

  1. The bigger the investment, the more data-analysis should be made in order to not loose millions
  2. 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.

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.


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 [1]. 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.


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.


  1. 1.0 1.1 1.2 1.3 1.4 1.5 1.6 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
  2. 2.0 2.1 Cooper, R.G., Edgett, S., Kleinschmidt, E. New Product Portfolio Management: Practices and Performance 16(4) p333–351, July 1999
  3. https://www.bcgperspectives.com/content/classics/strategy_the_product_portfolio
  4. 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
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