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Project Portfolio Management - What’s the Difference Between “Excellent,” “Good,” “Fair” and “Poor”

November 27, 2010 | Author: PM Hut | Filed under: Project Portfolio Management

Project Portfolio Management - What’s the Difference Between “Excellent,” “Good,” “Fair” and “Poor”
By George F. Huhn

It depends on whom you ask.

And that is why the common practice of assigning business values using simple categories such as “Excellent,” “Good,” “Fair,” and “Poor” to projects in project portfolios is usually misleading and wrong.

Why?

First, in most project portfolio management situations where this is used, there is no explicit quantified value assigned to each term, and team members are not calibrated or trained on how to make the assignments. Thus, each person is “on their own” to interpret how to make the assignment, and they can erroneously assume that other team members are doing it the same way.

Second, the categories often don’t capture meaningful differences between projects. For example, let’s say a team is using “Very High,” “High,” “Moderate,” and “Low” to assign project value. If a linear scale is assumed for projects valued between 0 and $1 million, “Low” means that a project value is between 0 and $200,000. Thus, this type of valuation essentially says that $200,000 and $0 are identical. Are they really?

Third, using these types of value assignments or even “on a scale of 1 to 10″ implies a straight-line relationship between the categories that is often not tested. Is a “Fair” project twice as valuable as a “Poor” project? Is a “Good” project three times as valuable as a “Poor” project?

Fourth, the interpretation of relative value can change as people go down a list of projects. For example, if they start to think that they have assigned too many projects as “High” they will go back and start re-assigning some of the “High’s” to “Good.” So the internal cognitive scale changes in the middle of making the assignments, and therefore, the value assignment becomes dependent on something other than an actual intrinsic value.

Therefore, even though the results of using such methodology may appear to be valid and understandable, when you start to scratch the surface, they often aren’t.

But there are some project portfolio management situations where assigning project values to categories is very useful, such as when you want to express preferences for non-quantifiable text categories such as geographical locations, colors, week days, zip codes, etc. However, this should only be done when you truly can’t use quantitative numerical values. If you can use quantitative inputs, such as for financial data, use numbers not categories.

To use categorical inputs properly, be sure that you assign specific values or relative values to each category so that the people making the assignments understand what their assignments mean. For example, a pharmaceutical company uses categories to express their preference for developing medicines for different therapeutic areas such as “cardiovascular,” “central nervous system” (CNS), “oncology,” etc. If they assign a value of “10″ to cardiovascular and “5″ to CNS, it is clear that the “cardiovascular” category is twice as important to their strategy as the “CNS” category.

Doing it this way allows you to use this type of real world data in your project portfolio model in a way that is both realistic and rigorous.

George F. Huhn, President of Data Machines, Inc, founded the company in 2000. Data Machines offers business applications and consulting to help businesses improve their performance through superior project portfolio valuation and optimization. George has authored or co-authored numerous papers and articles in publications ranging from The Journal of Organic Chemistry to Newsweek, and has delivered seminars and keynote addresses at events across the country. He also holds several U.S. patents, and has been written about in Chemical and Engineering News.

He holds an Executive Masters of Science degree in the Management of Technology from the Wharton School and the University of Pennsylvania. He is also a Moore Fellow in Technology Management at the University of Pennsylvania’s School of Engineering and Applied Science, and holds a B.S. degree in chemistry from Drexel University.

DataMachines.com offers a project portfolio management software tool called Optsee® for calculating project and project portfolio value even with uncertain data. By automatically analyzing your project portfolio in thousands of scenarios using easy-to-run Monte Carlo simulations and then optimizing against multiple constraints such as limited funding and resources, Optsee® quickly shows you your best, worst, and most-likely returns from an optimal portfolio.

Data Machines also offer a spreadsheet workbook for easily calculating the return on investment (ROI) for any project portfolio management tool.

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This entry was posted on Saturday, November 27th, 2010 and is filed under Project Portfolio Management. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.

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