Donald Rumsfeld, Secretary of Defense under Presidents Gerald Ford and George W. Bush once stated, “As we know, there are known knowns; there are things we know. We know also there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns—the ones we
don’t know we don’t know.” The “unknown unknowns” have the biggest impact on projects. They may not be likely to occur, but they have outsized consequences.
When analyzing project risk, we tend to leave these out of our analysis. However, if we are to provide realistic ranges of uncertainty in our cost and schedule estimates, we need to incorporate them. How can we do this? As usual, the wit and wisdom of Norman Augustine applies here. As he wrote in his classic book Augustine’s Laws, “Two types of uncertainty plague most efforts to introduce major new products: known-unknowns and unknown-unknowns. The known-unknowns, such as the composition of the moon’s surface at the exact location of the first Apollo landing, can be accommodated and an approach planned which minimizes their consequences. The second category, the unknown-unknowns, cannot be specifically identified in advance, but their existence in aggregate can be predicted with every bit as much confidence as insurance companies place in actuarial statistics.”
In other words, the true uncertainty is in the historical track record, as measured by cost growth and schedule delays. These are examples of realized risks, or what I like to call risk in action. Using historical cost and schedule growth data, we can see systematic variation that closely fits a three-parameter lognormal. This occurs for both cost and schedule. See the graph below. As a comparison, I also include the Gaussian, or “normal,” distribution.
The distribution was fit to schedule growth data using three parameters. The parameters that fit the data indicate that 90% of project schedules experience delays, significant risk in schedule delays, and little opportunity to make schedules much shorter than the baseline (at best 30%).
This has been implemented in Excel, in the attached spreadsheet, which you can download. For more information about ensuring realism in risk analysis, check out my forthcoming book Solving for Project Risk Management: Understanding the Critical Role of Uncertainty in Project Management. It is now available for pre-order from Amazon and Barnes and Noble, both in hardcover and ebook formats. You can download Chapter 1 for free.