Annual Planning – Part 1

In the Statistical PERT example workbook for Annual Planning, I estimated the costs of ten projects in a portfolio of projects.  The smallest project has a most likely cost of just \$35,000, while the biggest project has a most likely cost of \$1.5M.  Pretty big difference!  And each project is estimated using a PERT-styled, three-point estimate.  This gives a range of possible project costs, and for most of the projects, the probability curve is skewed to the right, meaning that there is a greater likelihood that project costs will be higher than the most likely project cost rather than lower.

The most likely project cost for the entire project portfolio is \$4.8M; that’s just the sum of the most likely project costs in Column C.  The expected value of the portfolio is higher, though.  We use the PERT formula to estimate the mean of the probability distribution, and that result is about \$4.9M.  We expected the expected value of the portfolio to be higher than the most likely cost because of right-side skewing of the ten projects in the portfolio.

Now, if we just created a budget based upon either the most likely portfolio cost of \$4.8M or the expected cost of \$4.9M, we very likely would not have enough funds to pay for all the project-related work in the portfolio.  Why not?  Because, although the mode is at the top of the curve, and the median is somewhere near the top, those points on the probability curve equate to around just 50% of all possible outcomes for the uncertainty.  In other words, budgeting using just the mode or the mean will lead to about a 50-50 chance of success.  Do you want to be right half the time?  I don’t!

Using Monte Carlo simulation of the same 10 projects (and 100,000 trials in the simulation), look at all the area under the curve to the right of the \$4.83M mean:

Budgeting money to equal just the mode or just the mean is a recipe for disaster.

What we need is a budget that is very unlikely to be exceeded by the actual costs of the projects, but not more than what we really need to get the projects done, either.  We don’t want to tie-up organizational reserves unnecessarily by putting too much money in the project portfolio budget, but we don’t want too little in the budget, either.

This is where confidence intervals come into play, along with high-probability planning estimates.  At the end of the day, the portfolio budget ought to have a high probability of meeting the needs of all ten projects.  That figure, whatever it is, should have a high likelihood of leading to budget success.