Academic journal article Research-Technology Management

Bringing Rigor and Reality to Early-Stage R&D Decisions: Just Because the Future Is Unknown Doesn't Mean Rigorous Investment Decisions Cannot Be Made

Academic journal article Research-Technology Management

Bringing Rigor and Reality to Early-Stage R&D Decisions: Just Because the Future Is Unknown Doesn't Mean Rigorous Investment Decisions Cannot Be Made

Article excerpt

In certain early-stage R&D efforts, even the most prescient technology manager cannot accurately forecast the ultimate commercial viability of the research. The commercial prospects of many ongoing research programs in biotechnology and nanotechnology, for example, are beyond prediction. No technology forecasting technique, no matter how advanced, can refute this simple truth: sometimes the unpredictable is just that--unpredictable.

Nonetheless, it is natural to hope that rigorous analysis might allow us to know the unknowable. After all, as R&D managers are increasingly held accountable for every dollar spent, they are being asked to generate forecasts that assess the likely return on investments (ROI) in even the most speculative R&D. Unfortunately, it is in these highly uncertain situations that judgments are most likely to be biased.

The emphasis on point-forecast-based ROI estimates forces managers to treat the unpredictable as predictable, and makes them susceptible to what psychologists call the planning fallacy (1). In its grip, managers make decisions based on delusional optimism, systematically overestimating the benefits and underestimating the costs of projects. As a result, they launch and continue to fund R&D initiatives that are unlikely to come in on budget or on time or to ever deliver the expected returns.

The Right Kind of Rigor

In one sense, the increased emphasis on explicit ROI analysis of even early-stage R&D efforts is a step forward. Such analyses impose a rigorous, systematic process on decisions that might otherwise be made more informally, with managers substituting gut instinct for explicit analysis of expected technology benefits and development costs.

However, when R&D decision-makers are faced with high uncertainty over these expected costs and benefits, as is often the case in early-stage research, this process is marginally helpful at best, and at worst, downright dangerous. In an attempt to generate the expected ROI of a new program, managers must often generate "best guesses" for technology cost and benefit parameters when the extant scientific literature and expert opinion have yet to coalesce around any one view of these parameters. The process of generating an ROI estimate forces managers to adopt an inside view, which focuses on the specifics of the case at hand--considering the firm's objective, the resources they bring to bear, and the obstacles to completion, in essence, they construct a future history of the project. The expected ROI is then based on what is seen as the "realistic" most likely scenario. This scenario (as well as the worst case scenario) is frequently optimistic. The problem is that even when this realistic scenario is the most likely outcome, it is still not very likely, considering the multifarious ways the R&D process as well as product commercialization can go wrong.

When the ROI process is reduced to a biased, point-forecast-generating exercise that ignores key uncertainties it can no longer be considered a rigorous methodology--all that is left is false rigor. At the same time, we know that "going with your gut" is hardly a rigorous alternative when faced with high uncertainty. How can managers make systematic, rigorous R&D investment decisions when the best possible analysis cannot generate reliable point forecasts?

First and foremost, managers must do their homework. Even in the most uncertain technology environments, analysis can usually penetrate the uncertainty and withdraw information that is useful for making investment decisions. It is this residual uncertainty the uncertainty left after the best possible analysis to separate the unknown from the unknowable that matters when assessing and managing risky, early-stage R&D ventures.

Second, R&D managers should characterize the level of residual uncertainty that they face (see illustration, this page). …

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