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


Cleaning the Crystal Ball

Reflecting risk in forecasts is a simple concept and one that may seem easy to put into practice, but managers commonly ignore the uncertainties and simply collapse their forecasts into averages instead. We often see this in predictions of project completion timelines. Consider a project with 10 parallel tasks. Each task should take between three and nine months, with an average completion time of six months for all of them. If the 10 tasks are independent and the durations are distributed according to a triangular distribution, chances are less than one in 1,000 that the project will be completed in six months, and the duration will be close to eight months. But using the six-month figure instead offers an almost irresistible temptation; after all, that’s the average input.

Despite the potential that point estimates carry for misleading decision makers, many firms default to them in forecasts. For example, Airbus and Boeing present passenger traffic and freight traffic annual growth rates over a 20-year horizon as point estimates in their respective biannual “Global Market Forecast” and “Current Market Outlook” reports. Although a close reading of the reports suggests that the forecasters considered ranges when generating the forecasts — and even conducted sensitivity analyses to understand the implications of different assumptions — such scenarios are not reported. A forecast showing the range and not just the average would be more valuable in making plans, and would help the industry avoid overconfidence.

In short, forecasting should not be treated as a game of chance, in which we win by getting closest to the eventual outcome. Occasionally being “right” with a particular prediction creates no real benefit and can in fact lead to a false sense of security. No one can produce correct point forecasts time and time again. Instead, it’s better to use the range of possible outcomes as a learning tool: a way to explore scenarios and to prepare for an inherently uncertain future.

Drivers of Uncertainty

The most useful forecasts do not merely document the range of uncertainties; they explain why the future may turn in different directions. They do this by “decomposing” the future into its component parts — the driving forces that determine the behavior of the system. Just asking “Why might this happen?” and “What would happen as a result?” helps to uncover possible outcomes that were previously unknown. Recasting the driving forces as metrics, in turn, leads to better forecasts.

For example, the general business cycle is a driving force that determines much of the demand in the appliance industry. Key economic metrics, such as housing starts, affect the sales of new units, but a consumer’s decision to replace or repair a broken dishwasher also depends on other factors related to the business cycle, such as levels of unemployment and consumer confidence. With metrics estimating these factors in hand, companies in that industry — including the Whirlpool Corporation in the U.S. and its leading European competitor, AB Electrolux — use sophisticated macroeconomic models to predict overall industry sales and, ultimately, their share of the sales.

Here, too, the effective use of metrics requires an embrace of uncertainty. Simply focusing on the output of the model (the projected sales figures) rather than the input (such as unemployment and consumer confidence) can actually do more harm than good. Whirlpool’s planners use their industry forecast models to focus executive attention, not replace it. The planners present the model for the upcoming year or quarter, describing the logic that has led them to choose these particular levels of demand and the reason the outcomes are meaningful. Executives can set plans that disagree with the forecasters’ predictions, but everyone has to agree on which input variables reflect an overly optimistic or pessimistic future. Even more important, managers can begin influencing some of the driving forces: For example, they can work with retail partners to encourage remodeling-driven demand to offset a drop in housing starts.

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