Strategy under uncertainty

Here is an article featured by The McKinsey Quarterly online, published by McKinsey & Company. As Hugh G. Courtney, Jane Kirkland,  and S. Patrick Viguerie explain in it, the traditional approach to strategy requires precise predictions and thus often leads executives to underestimate uncertainty. This can be downright dangerous. A four-level framework can help.

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At the heart of the traditional approach to strategy lies the assumption that executives, by applying a set of powerful analytic tools, can predict the future of any business accurately enough to choose a clear strategic direction for it. The process often involves underestimating uncertainty in order to lay out a vision of future events sufficiently precise to be captured in a discounted-cash-flow (DCF) analysis. When the future is truly uncertain, this approach is at best marginally helpful and at worst downright dangerous: underestimating uncertainty can lead to strategies that neither defend a company against the threats nor take advantage of the opportunities that higher levels of uncertainty provide. Another danger lies at the other extreme: if managers can’t find a strategy that works under traditional analysis, they may abandon the analytical rigor of their planning process altogether and base their decisions on gut instinct.

Making systematically sound strategic decisions under uncertainty requires an approach that avoids this dangerous binary view. Rarely do managers know absolutely nothing of strategic importance, even in the most uncertain environments. What follows is a framework for determining the level of uncertainty surrounding strategic decisions and for tailoring strategy to that uncertainty.

Four levels of uncertainty

Available strategically relevant information tends to fall into two categories. First, it is often possible to identify clear trends, such as market demographics, that can help define potential demand for a company’s future products or services. Second, if the right analyses are performed, many factors that are currently unknown to a company’s management are in fact knowable—for instance, performance attributes for current technologies, the elasticity of demand for certain stable categories of products, and competitors’ plans to expand capacity.

The uncertainty that remains after the best possible analysis has been undertaken is what we call residual uncertainty—for example, the outcome of an ongoing regulatory debate or the performance attributes of a technology still in development. But quite a bit can often be known despite this. In practice, we have found that the residual uncertainty facing most strategic-decision makers falls into one of four broad levels.

[Here is a discussion of Level One: A clear enough future. To read the complete article, please click here.]

The residual uncertainty is irrelevant to making strategic decisions at level one, so managers can develop a single forecast that is a sufficiently precise basis for their strategies. To help generate this usefully precise prediction of the future, managers can use the standard strategy tool kit: market research, analyses of competitors’ costs and capacity, value chain analysis, Michael Porter’s five-forces framework, and so on. A DCF model that incorporates those predictions can then be used to determine the value of alternative strategies.

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Hugh Courtney is a consultant in McKinsey’s Washington, DC, office; Jane Kirkland is an alumnus of the New York office; and Patrick Viguerie is a principal in the Atlanta office. This article is adapted from one that appeared in Harvard Business Review, November-December 1997. Copyright © 1997 President and Fellows of Harvard College. Reprinted by permission. All rights reserved.

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