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 / Summer 2010 / Issue 59(originally published by Booz & Company)


Cleaning the Crystal Ball

How intelligent forecasting can lead to better decision making.

 Peter Drucker once commented that “trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window.” Though we agree with Drucker that forecasting is hard, managers are constantly asked to predict the future — be it to project future product sales, anticipate company profits, or plan for investment returns. Good forecasts hold the key to good plans. Simply complaining about the difficulty does not help.

Nonetheless, few forecasters receive any formal training, or even expert apprenticeship. Too many companies treat the forecasting process like a carnival game of guessing someone’s weight. And given the frequency of sandbagged (deliberately underestimated) sales forecasts and managed earnings, we even wonder how often the scale is rigged. This lack of attention to the quality of forecasting is a shame, because an effective vehicle for looking ahead can make all the difference in the success of a long-term investment or strategic decision.

Competence in forecasting does not mean being able to predict the future with certainty. It means accepting the role that uncertainty plays in the world, engaging in a continuous improvement process of building your firm’s forecasting capability, and paving the way for corporate success. A good forecast leads, through either direct recommendations or informal conversation, to robust actions — actions that will be worth taking, no matter how the realities of the future unfold. In many cases, good forecasting involves recognizing, and sometimes shouting from the rooftops about, the inherent uncertainty of the estimates, and the fact that things can go very bad very quickly. Such shouts should not invoke the paranoia of Chicken Little’s falling sky; instead, they should promote the development of contingency plans to both manage risks and rapidly take advantage of unexpected opportunities.

Fortunately, better forecasting can be accomplished almost as simply as improving Drucker’s driving challenge. Turn on the headlights, focus on the road ahead, know the limits of both the car and the driver, and, if the road is particularly challenging, get a map — or even ask others for directions. By using the language of probability, a well-designed forecast helps managers understand future uncertainty so they can make better plans that inform ongoing decision making. We will explore the many approaches that forecasters can take to make their recommendations robust, even as they embrace the uncertainty of the real world.

The Flaw of Averages

In forecasting the future, most companies focus on single-point estimates: They propose a number for the market size or the company’s unit sales in the coming year, typically based on an average of expected data. Though companies generally manage against a specific target like revenue or profit, and also share that information with outside analysts, we often forget that a point forecast is almost certainly wrong; an exact realization of a specific number is nearly impossible.

This problem is described at length by Sam Savage, an academic and consultant based at Stanford University, in The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty (Wiley, 2009). He notes how focusing on an average without understanding the impact of the range can lead to flawed estimates. Better decisions result from taking the time to anticipate the likelihood of overshooting or undershooting the point, and then considering what to do today, given the range of possibilities in the future.

Savage highlights the simple example of a manager estimating the demand for 100,000 units of a product — based on a range of possible market conditions — and then extrapolating that average to produce a profit estimate. But the plausible demand could be as much as 50 percent above or below the average, with potentially dangerous consequences. If demand runs 50 percent above the average, the plant will miss some sales because it will be unable to increase capacity that much in the time period. Conversely, if demand runs 50 percent below the forecast average demand, the profit per unit will be dramatically lower, since the plant has to spread its fixed cost over fewer units. As a result, the profits at an average demand level will be much different from an average of the profits across the range of possibilities. Rather than a simple average, a better forecast would present a wide range of scenarios coupled with a set of potential actions to influence the demand and profitability. Such a forecast would encourage management to heed early signals of consumer interest to accelerate marketing and/or cut fixed costs if sales fall short, or to ramp up production quickly if sales appear to be at the high end of the forecast.

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