THE CHANGING LANDSCAPE
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 under- taken 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.
Level one: A clear enough future
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.
Level two: Alternative futures
The future can be described as one of a few discrete scenarios at level two. Analysis can’t identify which outcome will actually come to pass, though it may help establish probabilities. Most important, some, if not all, elements of the strategy would change if the outcome were predictable.
Many businesses facing major regulatory or legislative change confront level two uncertainty. Consider US long-distance telephone providers in late 1995,