Question

explain the higher one goes in an organization hierarchy, the greater the uncertainty

Answer 1

What makes for a good strategy in highly uncertain business environments? Some executives seek to shape the future with high-stakes bets. Eastman Kodak Company, for example, is spending $500 million per year to develop an array of digital photography products that it hopes will fundamentally change the way people create, store, and view pictures. Meanwhile, Hewlett-Packard Company is investing $50 million per year to pursue a rival vision centered around home-based photo printers. The business press loves to hype such industry-shaping strategies because of their potential to create enormous wealth, but the sober reality is that most companies lack the industry position, assets, or appetite for risk necessary to make such strategies work.

More risk-averse executives hedge their bets by making a number of smaller investments. In pursuit of growth opportunities in emerging markets, for example, many consumer-product companies are forging limited operational or distribution alliances. But it’s often

difficult to determine if such limited investments truly reserve the right to play in these countries or just reserve the right to lose.

Alternatively, some executives favor investments in flexibility that allow their companies to adapt quickly as markets evolve. But the costs of establishing such flexibility can be high. Moreover, taking a wait-and-see strategy—postponing large investments until the future becomes clear—can create a window of opportunity for competitors.

How should executives facing great uncertainty decide whether to bet big, hedge, or wait and see? Chances are, traditional strategic-planning processes won’t help much. The standard practice is to lay out a vision of future events precise enough to be captured in a discounted-cash-flow analysis. Of course, managers can discuss alternative scenarios and test how sensitive their forecasts are to changes in key variables, but the goal of such analysis is often to find the most likely outcome and create a strategy based on it. That approach serves companies well in relatively stable business environments. But when there is greater uncertainty about the future, it is at best marginally helpful and at worst downright dangerous.

Under uncertainty, traditional approaches to strategic planning can be downright dangerous.

One danger is that this traditional approach leads executives to view uncertainty in a binary way—to assume that the world is either certain, and therefore open to precise predictions about the future, or uncertain, and therefore completely unpredictable. Planning or capital-budgeting processes that require point forecasts force managers to bury underlying uncertainties in their cash flows. Such systems clearly push managers to underestimate uncertainty in order to make a compelling case for their strategy.

Underestimating uncertainty can lead to strategies that neither defend against the threats nor take advantage of the opportunities that higher levels of uncertainty may provide. In one of the most colossal underestimations in business history, Kenneth H. Olsen, then president of Digital Equipment Corporation, announced in 1977 that “there is no reason for any individual to have a computer in their home.” The explosion in the personal computer market was not inevitable in 1977, but it was certainly within the range of possibilities that industry experts were discussing at the time.

At the other extreme, assuming that the world is entirely unpredictable can lead managers to abandon the analytical rigor of their traditional planning processes altogether and base their strategic decisions primarily on gut instinct. This “just do it” approach to strategy can cause executives to place misinformed bets on emerging products or markets that result in record write-offs. Those who took the plunge and invested in home banking in the early 1980s immediately come to mind.

Risk-averse managers who think they are in very uncertain environments don’t trust their gut instincts and suffer from decision paralysis. They avoid making critical strategic decisions about the products, markets, and technologies they should develop. They focus instead on reengineering, quality management, or internal cost-reduction programs. Although valuable, those programs are not substitutes for strategy.

Making systematically sound strategic decisions under uncertainty requires a different approach—one that avoids this dangerous binary view. It is rare that managers know absolutely nothing of strategic importance, even in the most uncertain environments. In fact, they usually can identify a range of potential outcomes or even a discrete set of scenarios. This simple insight is extremely powerful because determining which strategy is best, and what process should be used to develop it, depend vitally on the level of uncertainty a company faces.

What follows, then, is a framework for determining the level of uncertainty surrounding strategic decisions and for tailoring strategy to that uncertainty. No approach can make the challenges of uncertainty go away, but this one offers practical guidance that will lead to more informed and confident strategic decisions.

Four Levels of Uncertainty

Even the most uncertain business environments contain a lot of strategically relevant information. First, it is often possible to identify clear trends, such as market demographics, that can help define potential demand for future products or services. Second, there is usually a host of factors that are currently unknown but that are in fact knowable—that could be known if the right analysis were done. Performance attributes for current technologies, elasticities of demand for certain stable categories of products, and competitors’ capacity-expansion plans are variables that are often unknown, but not entirely unknowable.

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

Level 1: A Clear-Enough Future.

At level 1, managers can develop a single forecast of the future that is precise enough for strategy development. Although it will be inexact to the degree that all business environments are inherently uncertain, the forecast will be sufficiently narrow to point to a single strategic direction. In other words, at level 1, the residual uncertainty is irrelevant to making strategic decisions.

Consider a major airline trying to develop a strategic response to the entry of a low-cost, no-frills competitor into one of its hub airports. Should it respond with a low-cost service of its own? Should it cede the low-cost niche segments to the new entrant? Or should it compete aggressively on price and service in an attempt to drive the entrant out of the market?

To make that strategic decision, the airline’s executives need market research on the size of different customer segments and the likely response of each segment to different combinations of pricing and service. They also need to know how much it costs the competitor to serve, and how much capacity the competitor has for, every route in question. Finally, the executives need to know the new entrant’s competitive objectives to anticipate how it would respond to any strategic moves their airline might make. In today’s U.S. airline industry, such information is either known already or is possible to know. It might not be easy to obtain—it might require new market research, for example—but it is inherently knowable. And once that information is known, residual uncertainty would be limited, and the incumbent airline would be able to build a confident business case around its strategy.

Level 2: Alternate Futures.

At level 2, the future can be described as one of a few alternate outcomes, or discrete scenarios. Analysis cannot identify which outcome will occur, although 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 2 uncertainty. Consider U.S. long-distance telephone providers in late 1995, as they began developing strategies for entering local telephone markets. By late 1995, legislation that would fundamentally deregulate the industry was pending in Congress, and the broad form that new regulations would take was fairly clear to most industry observers. But whether or not the legislation was going to pass and how quickly it would be implemented in the event it did pass were uncertain. No amount of analysis would allow the long-distance carriers to predict those outcomes, and the correct course of action—for example, the timing of investments in network infrastructure—depended on which outcome occurred.

In another common level 2 situation, the value of a strategy depends mainly on competitors’ strategies, and those cannot yet be observed or predicted. For example, in oligopoly markets, such as those for pulp and paper, chemicals, and basic raw materials, the primary uncertainty is often competitors’ plans for expanding capacity: Will they build new plants or not? Economies of scale often dictate that any plant built would be quite large and would be likely to have a significant impact on industry prices and profitability. Therefore, any one company’s decision to build a plant is often contingent on competitors’ decisions. This is a classic level 2 situation: The possible outcomes are discrete and clear. It is difficult to predict which one will occur. And the best strategy depends on which one does occur.

Level 3: A Range of Futures.

At level 3, a range of potential futures can be identified. That range is defined by a limited number of key variables, but the actual outcome may lie anywhere along a continuum bounded by that range. There are no natural discrete scenarios. As in level 2, some, and possibly all, elements of the strategy would change if the outcome were predictable.

Companies in emerging industries or entering new geographic markets often face level 3 uncertainty. Consider a European consumer-goods company deciding whether to introduce its products to the Indian market. The best possible market research might identify only a broad range of potential customer-penetration rates—say, from 10% to 30%—and there would be no obvious scenarios within that range. Such a broad range of estimates would be common when introducing completely new products and services to a market, and therefore determining the level of latent demand is very difficult. The company entering India would be likely to follow a very different and more aggressive entry strategy if it knew for certain that its customer penetration rates would be closer to 30% than to 10%.

Analogous problems exist for companies in fields driven by technological innovation, such as the semiconductor industry. When deciding whether to invest in a new technology, producers can often estimate only a broad range of potential cost and performance attributes for the technology, and the overall profitability of the investment depends on those attributes.

Level 4: True Ambiguity.

At level 4, multiple dimensions of uncertainty interact to create an environment that is virtually impossible to predict. Unlike in level 3 situations, the range of potential outcomes cannot be identified, let alone scenarios within that range. It might not even be possible to identify, much less predict, all the relevant variables that will define the future.