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Volume 19, Issue 39;   September 25, 2019: Planning Disappointments

Planning Disappointments

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When we plan projects, we make estimates of total costs and expected delivery dates. Often these estimates are so wrong — in the wrong direction — that we might as well be planning disappointments. Why is this?
The Leonard P. Zakim Bunker Hill Bridge

One of the many problems contributing to cost overruns in the highway project known as Boston's "Big Dig" was a sequence of delays in determining the design of the Charles River Crossing. Once this decision-making overran its schedule, very serious problems developed later in the project, in both management and engineering and construction. As in the private sector, the early focus of scrutiny has been on the engineering and construction rather than on management, in part because we measure them more carefully. Pictured is the Leonard P. Zakim Bunker Hill Bridge in a later construction phase, as it emerges from the Thomas P. "Tip" O'Neill Jr. Tunnel to cross the Charles River. This particular crossing is very near the one referred to as "two if by sea," in the poem, "The Midnight Ride of Paul Revere," by Longfellow (1860). Photo courtesy Massachusetts Turnpike Authority.

When people estimate how long a piece of work will take — or how much it will cost — their estimates are fairly reliable if they've done lots of similar work before. But if the work is unfamiliar, or if there are significant unknowns associated with the work, estimates of cost and schedule tend to be far less accurate, even when all we asked for was a "rough estimate." And the inaccuracies are almost always of a certain kind: final costs tend to exceed estimates, and deliveries are often late.

Why does this happen?

Last time, I explored three important contributors to this pattern — the planning fallacy, self-serving bias, and organizational politics. But in addition to these sources of systematic errors, there are two conceptual errors that are a bit mathematical in nature. One is a general misunderstanding of the term "rough estimate," and the second is a misconception about the shape of probability distributions of costs and schedules for unfamiliar projects.

Misunderstanding the term "rough estimate"
Rough estimates can be useful if we understand the true meaning of "rough." Technically, a rough estimate is an estimate that has a relatively wide margin of error. Every estimated value has a band of values that corresponds to, say, 95% confidence, and a mean or average that corresponds to a point within that band. If the width of the 95% confidence band is W, and the mean is M, then there is a ratio R equal to W/M. The "rougher" the estimate is, the greater is R.
To be safe, users of rough estimates need to use the value at the top end of the band for estimated values in which larger is worse, or the value at the low end of the band for estimated values in which smaller is worse. Too often, people just use M. They can be forgiven, of course, when the estimator provides M alone, without reference to a 95% confidence band.
There are clear reasons for this. In my experience, many consumers of estimates — the decision-makers, as they're often called — are uncomfortable with any estimate that includes an uncertainty band. I've actually heard reports about decision-makers' responses to estimates of the form, say, 8.7 million +/- 1.5 million, that go like this: "Don't give me this +/- stuff! If you don't know how much it will cost, just admit it! Now give me an estimate! Exactly what will this cost?"
In fairness, Rough estimates can be useful
if we understand the true
meaning of "rough"
many estimators lack sufficient information needed to compute a 95% confidence band. Others aren't sure how to calculate it. Others are intimidated by decision-makers who reject the "+/- stuff," and still others aren't intimidated, but just don't want to fight with resistant decision-makers. Whatever the case, estimates without 95% confidence bands are common, and because the estimates lack these bands, overruns are defined as "less favorable than the so-called estimate," which makes overruns very likely.
The shape of probability distributions of costs and schedules
Another source of estimation error is our human intuition about the shape of the probability distributions of costs and schedules. We tend to think of these distributions as fairly symmetrical around some average, or mean. And we also like to believe that the mean of, say, the cost distribution, is fairly close to the most likely value of that distribution.
We base this intuition on our experience with familiar efforts. And as it happens, for familiar efforts, our experience is a useful guide. That is, the distribution of cost or schedule outcomes for familiar efforts is fairly symmetric around some average value.
But for unfamiliar efforts, our intuition is misleading — sometimes grossly misleading. And this comes about for reasons that are easy to understand intellectually, but difficult to incorporate into our intuition.
With unfamiliar efforts, or efforts that are subject to significant unknowns, the probability distributions for costs and schedules aren't symmetric about their means. In fact, they're wildly asymmetric. While there are some pretty hard limits on how quickly or cheaply things can happen, there are no analogous limits on how slowly or expensively things can happen. For example, no matter how hard you try, you probably cannot get to the conference room in the other building in less than 45 seconds — even if you run. But without trying to dawdle, it can sometimes take you 20 minutes to get there, even though it's only a five-minute walk, depending on the weather, who you run into on the way over, and whether or not you have to go back to pick up something you forgot.
For unfamiliar projects, or projects subject to significant unknowns, the probability distributions for costs and schedules tend to have long "tails" corresponding to unanticipated costs and delays. These tails cause the distributions to have average values that can be much greater than their most likely values. And that's what throws off our intuitions. When we estimate, we tend to estimate the most likely value. And when the average value is much greater than the most likely value, our estimates tend to fall crazily short. The situation is even worse when we stack one task or project after another in a long train, because the uncertainty bands stack up.

Next time you make or receive an estimate without a 95% confidence band, ask yourself, "What are the lowest and highest values that wouldn't shock me totally senseless beyond all reason?" That should give you some idea of what 95% confidence means. Go to top Top  Next issue: Start Anywhere  Next Issue

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