The Bayes' Law PPM Model
Can you use the new model?
Because PPM results represent a non-random selection of proposals, you must
analyze PPM results with techniques that match your method of selecting projects.
You can use the new PPM model if you select proposals by (1) evaluating proposals,
(2) prioritizing proposals, (3) selecting down the ranking and (4) making
changes for various reasons. The changes
must not be too extensive, and you must evaluate projects with an interval or
ratio scale. For example, you can use financial metrics, decision analysis models,
the analytic hierarchy process (AHP) or scoring models. You will get the best
results if you classify proposals into strategic buckets and then prioritize and
select from each bucket. If you use optimization, the model may work for you as
While some metrics require less data, measuring the quality of your project
prioritization requires data from at least fifty project proposals and thirty-five
funded projects. These requirements are not as extreme as they appear. The new
model analyzes PPM results, so you can use past PPM data. Suppose data from the
past two years is relevant. You can use the model if, on average, a strategic
bucket evaluates twenty-five proposals and funds eighteen projects annually. If
data from the past three years is relevant, a strategic bucket must evaluate seventeen
proposals and fund twelve projects each year. Depending upon the outcome of my
research, you may be able to pool data by combining data from several strategic
Bayes' Law and PPM
If you evaluate PPM results, you will evaluate a funded project (at least)
twice: once before project selection and again after the project is completed.
When a project is evaluated before selection, I refer to it as a proposal;
when it is evaluated after selection, I refer to it as a completed project.
I use these terms even though some proposals and "completed" projects are currently
The most important feedback metrics require a simple evaluation of completed
projects. You must classify them into two categories: Good and Bad. You can define
Good and Bad in any way that suits your business. The categories apply to proposals
as well. A Good proposal is one that, if it is funded, will produce a Good completed
project. A Bad proposal is one that, if it is funded, will produce a Bad completed
With these definitions in mind, the odds version of Bayes' law governs PPM
is the fraction of proposals that are Good proposals.
- is the
fraction of completed projects that are Good projects.
- is the quality
of project selection
Obviously, high values of
boost financial performance. When
is high you have more Good projects creating value and less Bad projects wasting
resources. The next sections reveal how
and affect .
Note: Note the sequence advocated here by the author, namely: "(1) evaluating
proposals, (2) prioritizing proposals, (3) selecting down the ranking
and (4) making changes for various reasons." By way of comparison, the Project
Management Institute's Standard for Portfolio Management calls for: "Identify;
Categorize; Evaluate; Select; Prioritize; Balance ..." (Chapter 3,
Section 3.3, 2008). If you Select then Prioritize rather than
Prioritize then Select, as required by the author,
your results may be impacted to some degree.