Monthly Archives: October 2016

Palisade Risk Conference 2016


I’ll be heading to New Orleans for the first time in my life in a few weeks.  The Palisade Risk Conference 2016 is being held on November 1 & 2, 2016.  I have used Palisade’s DecisionTools Suite since my first year in graduate school (in the MSPM program at George Washington University) back in 2010.  I love the power and utility of @Risk, the Excel add-in program for doing Monte Carlo simulations.  @Risk has helped me create Statistical PERT because I use @Risk simulations to compare against and validate my Statistical PERT approach and results.  Without @Risk, there would be no Statistical PERT.

I liken Statistical PERT to a screwdriver:  simple, easy-to-use, solves a lot of problems, inexpensive (Statistical PERT is free, actually).  By contrast, @Risk is complex, not-as-easy-to-use (but still easy for rudimentary modeling), solves a whole lot more problems, but is expensive.  Whereas everyone has a screwdriver in their junk drawer or garage, not everyone has, say, a miter saw or some other, expensive power tool.

If you’re planning attending the Risk Conference in New Orleans, hit me up!

Predicting vs. Forecasting (Part 2)


Yesterday, Hurricane Matthew swept passed my home in Boca Raton, Florida.  For the U.S., it’s caused some property damage and a few people died because 911 personnel couldn’t get to those few that had life-endangering emergencies.  In Haiti, the storm wreaked havoc on that poor nation, and hundreds have died.  ;'(

Weather forecasters make forecasts.  They make predictions, too, but we don’t call it weather predicting, we call it weather forecasting.  What’s the difference?

A prediction is a single outcome of what a future uncertainty looks like.  It ignores the possible many other outcomes, some of which are probable, some of which are improbable.

Forecasting, however, recognizes that there are many possible, future outcomes for a given uncertainty.  Some of those outcomes are improbable, some are more probable.

For hurricanes, weather forecasters use the familiar “cone of uncertainty” which looks like a funnel.  The narrow part of the funnel is the expected path of the eye of the hurricane that’s nearest to where the eye of the hurricane currently is.  The wide part of the cone or funnel is three of five days away.  Anyone who is familiar with agile estimation is likely familiar with the cone of uncertainty because it works the same way.  Agile teams can pretty accurately predict what their velocity will be in the next sprint, but it’s hard to estimate what they’ll get done three months from now.

Project managers ought to become skilled at creating project forecasts instead of project predictions.  We may still need to create predictions for schedule and budget for our project sponsors who authorize and fund projects, but the better way to align expectations among all key stakeholders and improve executive decision-making is to make forecasts — not predictions.

If we have to offer predictions — a single budget number for a project, or a single date on which a project will be complete — we ought to at least offer that budget number or calendar date with a confidence level:  “With 90% certainty, the project will cost $800,000 or less, and, with 90% confidence, we will finish the project by March 31.”

When we share predictions with calculated confidence levels, we implicitly allow that the prediction may not come to pass (and how likely is that risk).  If a project sponsor demands greater assurance that the project will be done, we can offer other, more confident predictions (which naturally cost more money and take more time).  If a project sponsor wants to shrink the budget and/or schedule, we can do that too — and then share the risk that the budget and schedule will fail using easily understood probabilities.

Version 1.4 Released!

Today, I realized Version 1.4 of Statistical PERT – Normal Edition for all SPERT example workbooks and templates (task duration, expense, revenue, agile, project portfolios, events).

Here’s what’s new in this release:

  • I removed the “Unskewed” probabilities which used the mode instead of the mean.  While using the mode would, under some conditions, yield a more accurate probability than using the mean, I found that most people were just confused by the difference between using the mode and using the mean.  In most cases, using the PERT mean will give accurate results, so I decided to remove the confusion by removing the columns devoted to “Unskewed” probabilities.
  • There is a new pie chart and associated cells which lets estimators create confidence intervals for any selected confidence level.  You can specify a confidence level and the confidence interval is determined for you, or, optionally, you can give a confidence interval and the confidence level is calculated for you.
  • There is a new pie chart showing what uncertainty looks like with respect to how likely is the Most Likely outcome.  One of Statistical PERT’s unique attributes is that it allows estimators to use subjective judgment (private knowledge, gut instinct, emotions, intuition) to rationally adjust the SPERT estimates.  Many people don’t know how to choose a subjective judgment, though.  This new pie chart shows what each subjective choice looks like if the only three outcomes were the minimum, most likely and maximum point-estimates.  Interestingly, there was a PMI Global Congress presentation that talked about visually making risk-based choices.
  • I standardized the SPERT example workbooks and templates so future changes can be easily made across all the different SPERT example workbooks and templates.  So, instead of the task duration’s template having headings like Optimistic, Most Likely and Pessimistic (where the order would be reversed for the Revenue template), now all templates use Minimum, Most Likely and Maximum as the headings for the three-point estimate columns.
  • Each SPERT file now has a Welcome! tab that introduces Statistical PERT to the downloader, and it has a link to a Quick Start guide that can help to quickly learn how to use any SPERT download.
  • I made a number of other tweaks, too, of a minor nature.  SPERT, for example, is now a federally registered trademark.

If you haven’t already done so, I encourage you to download your favorite SPERT example workbook or template!