The ten project portfolio I’ve been examining (which is the SPERT example workbook on the download page) has a maximum cost of **$6.1M**. That’s the sum of all the maximum values. If our 3-point estimates are reasonably accurate, then we’d need just over $6M in the annual plan to fully fund all ten projects.

But doing that would be a bad idea, because the likelihood of needing $6M is extremely small. We’d unnecessarily tie-up organizational resources in a project portfolio budget.

What we need is a portfolio budget that has a high likelihood of fulfilling the budgetary needs of all ten projects. How high of a likelihood? **That’s a judgment call.** Asking for a portfolio budget that has a 95% likelihood of success seems like a good idea. Or maybe just 90%. You probably wouldn’t want to go much lower than 90% unless you’re willing to accept that the budget won’t be sufficient, and the resulting impacts of busting the budget aren’t too frightening.

Let’s suppose we want a portfolio budget that has a 95% likelihood of success. That means that the budget will meet or exceed the actual needs of all ten projects 95% of the time. (Of course, that statement is a little nonsensical because there is only “one time” we are executing these ten projects — in the next annual cycle. So I’d be better off saying that we want a portfolio that has a 95% likelihood of meeting or exceeding the actual project needs for all ten projects).

Okay, if I want a portfolio budget that will fully fund all ten projects with 95% confidence, does that mean I need to budget each project with 95% confidence? On the surface, that might make sense. If every project has a budget that is 95% certain to meet the needs of the project, then wouldn’t the whole portfolio have a 95% chance of being sufficient?

The short answer is, no. Statistics don’t work that way. Since the mean and mode for a bell-shaped probability have about a 50% chance of meeting or exceeding the actual outcome for that uncertainty, budgeting at something greater than 50% will create a buffer, a sort of reserve, created by any instance when we didn’t use all the budget allocated for the project (which should happen with greater than a 50-50 probability)..

In a portfolio with ten projects, if they all are budgeted at, say, with a 75% confidence planning estimate, then we expect that one-fourth of the time, projects in the portfolio will bust their budgets (some by a very little, some by a lot). However, if projects truly followed bell-shaped outcomes, then we’d have nearly half the projects in the portfolio cost less than either the most likely project cost (the mode) or the expected value of the project (the mean). And those cost-saving projects that finish with actual costs that are less than either the mode or the mean should offset that 25% of projects that finish greater than the portfolio budget that had 75% confidence. Make sense?

Unfortunately, projects rarely finish sooner than their planned finish dates, and they seldom cost less than their planned budgets. (A topic for another day is what to do about that, but the short answer is in my Pluralsight course where I discuss the Unified Scheduling Method).

But here’s the happy result of using project planning estimates that are greater than the expected value (the mean): you only have to create planning estimates **with about 75% confidence** to create a ten-project portfolio that has about** 95% confidence**. Put another way, it’s highly unlikely that so many projects will exceed their 75% planning estimates that there’s nothing left in the portfolio budget. We expect maybe two or three projects to exceed their planning estimates in a ten-project portfolio (25% of 10 is 2.5, right?), but it’s highly improbable that five of ten projects will fail their 75% planning estimates.

Let’s continue to look at this in the next post.