Estimating the time and cost it takes to deliver a project is the bane of system development and it is an old problem that doesn’t seem to be getting any better. How bad? According to a 2012 McKinsey-Oxford University study of 5,400 large- scale IT projects, 66 percent were over budget, 33 percent came in late, and 17 percent delivered less functionality than they promised.
It’s not for a lack of trying. Dozens of estimating approaches and tools have been developed over the years. History-based estimating approaches look into the organization’s past and use the effort required for similar completed projects. Formula-based approaches require the project manager to answer a number of questions that are then entered into a mathematical model. Expert- or guru-based estimating approaches gather systems development and business experts together and, in an IT version of a séance, divine the effort required. Experimental-based approaches involve performing a small amount of actual work on the project, stop- ping, measuring progress, and then projecting the effort needed to complete the project.
The result: In spite of all these approaches, project estimates are still wildly inaccurate. Worse, the estimates are not uniformly incorrect, but skewed with the number of over budget/late projects significantly greater than the number of under budget/early ones. Something strange is going on, but what?
Perhaps this is not an intellectual problem but an instinctual one. Maybe, someday some evolutionary biologist will discover the underestimation gene—the DNA that causes our species to underestimate any task. Why might we have such a gene? Conceivably, back in our prehistoric past, if we had really understood how difficult some tasks were we would never have undertaken them.
Imagine if our cave-dwelling ancestor said, “I think I’ll invent the wheel today,” only to have his neighbor one cave over say, “Don’t forget that you need to reduce the friction between the hub and the axle?” It would be understandable if our discouraged ancestor put wheel-inventing aside for another day.
This useful skill of underestimating effort might have been passed down generation to generation so that now the do-it-yourselfer is convinced he can assemble that bookcase in the directions-predicted 2 hours. Unfinished bookcases might be a testimony to our genetic past. Our estimation-challenged brains don’t see the potential problems, but only a perfect result.
Planning for the perfect is the realization that when one estimates the effort of anything, in the estimator’s mind is the picture of how the project will unfold if everything goes perfectly.
If we are programmed to underestimate effort, then simply treating poor estimates as an educational problem will continue to prove disappoint- ing. Simple training is no substitute for gene therapy. Breaking this deterministic hold will require a different approach.
Until gene splicing can solve this problem we need an interim solution. Our best response to the estimation conundrum is not to reject the inevitable, but to embrace it. Recognize that you are never going to be a good estimator—your genes won’t allow it. But you can beat those genes at their own game.
This is how. Planning for the perfect leads to an ideal estimate. However, projects usually take longer—the actual is the idealistic estimate plus a factor X, call it the reality factor. If we know the reality factor, then the most realistic estimate is the ideal estimate plus an adjustment derived from the reality factor. If the ideal estimate is 100 person- months and the reality factor is 15 percent, then the realistic estimate is 115 person-months.
The reality factor needs to be revisited EVERY time actuals become available. The actuals need to be compared with the estimate, and a revised reality factor created. A reliable reality factor should start to emerge after just a few estimates and their comparison to actuals.
Now go and build that bookcase… however long it takes.
This article is excerpted from Tillmann’s book, “Project Management Scholia: Recognizing and Avoiding Project Management’s Biggest Mistakes” (Stockbridge Press, 2019)