This Guest paper was submitted for publication and is copyright to Gary J. Summers © 2009
Published October 2009

Introduction | The Bayes' Law PPM Model | Proposals and Selection
New PPM Metrics | Management | Improving Your PPM Situation | Conclusion

Improving Your PPM Situation

Portfolios are not created in a vacuum. When you create a portfolio you select from a set of proposals while striving to realize corporate goals. Subsequently, your company's project management capabilities determine how well your portfolio is executed. You should think of PPM as a set of PPM decisions (project selection and resource allocation) that occur within the context of a PPM situation (existing proposals, strategic goals and project management capabilities). Much of the PPM literature, education and practices focus on PPM decisions. The field gives scant attention to improving PPM situations. However, improving your PPM situation may be the best way to create value. For example, the value created by improving your proposal processes, thereby providing you with better choices, may exceed the value created by improving your project selection.

PPM is not just about portfolio creation.
Executives must improve all the business processes that
affect their portfolios.

Feedback from PPM results can help you improve your PPM situation. The new model provides metrics that will help you evaluate and improve your proposal processes (to raise P-proposals) and your project evaluations (to improve prioritization and QPS). These improvements will relax the aforementioned trade-off between strategic and financial goals. You will be able to fund more projects while maintaining a high ROI for your portfolio.

Improving Prioritization and Project Evaluation

Figure 11 estimates the benefits of improving prioritization. For the same strategic bucket, it shows the NPVs available from your current prioritization and the NPVs that would be available from an improved prioritization.

With better prioritization you can select more proposals and still achieve your financial goals.
Figure 11: How the quality of prioritization affects a buckets financial prospects
Figure 11: How the quality of prioritization affects a buckets financial prospects

If you evaluate projects with a scoring model, PPM results can help you improve your model, and thus your prioritization. By analyzing PPM results, you can set your model's attribute weights to maximize the model's ability to distinguish Good from Bad projects. Likewise, you can set the weights to maximize the correlation between proposal scores and the value created by projects.

Furthermore, if an optimal weight is small, that attribute is not helping you select proposals. Either you are evaluating the attribute poorly or the attribute's scale is faulty. In the first case, you must invest in resolving more uncertainty during the proposal process. In the second case, you must fix the attribute scale.

Scoring models differ from financial metrics and decision analysis models. Scoring models make predictions based on statistical relationships, analogous to linear regression. Financial metrics and decision analysis models are physics models. Presumably, PPM results can help you improve these models as well. For example, by analyzing PPM results you might estimate the accuracy and precision of key variables in your model. These estimates will identify aspects of the model that need improvement.

Improving Proposal Processes

In addition to improving your evaluations, you can improve your PPM situation by developing your proposal processes. This strategy is often overlooked in PPM, but better proposal processes will raise P-proposals and create value.

Histograms of project scores will help you evaluate and improve your proposal processes. Figure 12 illustrates histograms for incremental innovations (left) and for major innovations (right). Each histogram shows the number of proposals having each score, the average score (solid vertical line), the cutoff value used to select projects (dashed vertical line) and the variance of the proposal scores.

Figure 12: Histograms of proposal scores
Figure 12: Histograms of proposal scores

Incremental innovations face less uncertainty (than major innovation), so proposal processes should consistently produce good ideas. As a result, a histogram of proposal scores should have a high average score and a small variance (left hand Figure). If the histogram has a low average, you must improve your proposal processes. These improvements will raise the average score, shift the histogram to the right and raise P-proposals.

Major innovations face greater uncertainty, so few ideas will succeed. This is all right. When pursuing major innovations, asking for consistently good ideas is asking for failure. Because some ideas are poor ones, the histogram for major innovations has a low average and a high variance. If it has a high average, you must inspire and challenge your managers and staff to be bolder. Importantly, because some ideas will be poor ones, you cannot raise the of major innovations by increasing the average score. Instead, you must raise the P-proposals variance. Greater variation will extend the right tail of the distribution, raising P-proposals.

Management  Management

Home | Issacons | PM Glossary | Papers & Books | Max's Musings
Guest Articles | Contact Info | Search My Site | Site Map | Top of Page