OVERVIEW:This considerably simplified method for finding optimal R&D portfolios requires a minimum of information about the individual projects, namely the probability of a successful/unsuccessful project and the monetary returns in each case. The model requires only the assumption that the decision maker is risk averse. Linear programming, which was required in an earlier version, is replaced with a simple tree diagram that allows the user to readily uncover the nondominated solutions. This methodology is applied to an artificial five-project portfolio and to a real-world 30-project portfolio.

To date no single model has been published which fully satisfies the needs for a practical R&D project selection technique. Some earlier models cannot handle risk well, while others do not provide efficient portfolios. This paper will present a model, adapted from the literature of financial portfolio optimization, which provides a practical means of developing preferred portfolios of risky R&D projects. The method is simple and highly intuitive, requiring estimation of only two parameters, the expected return and the Gini coefficient. The Gini coefficient essentially replaces the variance in the two-parameter mean–variance model and results in a superior screening ability. The model that we present requires estimates of only these two parameters and, in turn, allows for relatively simple determination of stochastic dominance (SD) among candidate R&D portfolios. We apply our model to a simple artificial five-project set and then to a set of 30 actual candidate projects from an anonymous operating company. We demonstrate that we can determine the stochastically non-dominated portfolios for this real-world set of projects. Our technique, appropriate for all risk-averse decision makers, permits R&D managers to screen large numbers of candidate portfolios to discover those which they would prefer under the criteria of SD.

This paper describes a methodology for the selection of research and development (R&D) projects to add to or remove from an existing R&D portfolio, The analysis uses the criterion of conditional stochastic dominance to make selection recommendations. This criterion takes into account the effect of a given project on the risk and return of the existing portfolio. We use a methodology previously employed to analyze stock portfolios; however, we apply it using simulation in an R&D portfolio context. We apply the methodology to the portfolios of two actual companies and find that it generates priorities very close to those developed by internal company heuristics, We conclude that this methodology can be applied appropriately in these circumstances and that its recommendations are consistent with observed derision maker behavior. Our results suggest that an R&D manager should not consider project selection decisions in isolation, but, following this methodology, should take into account the context of the existing portfolio.

R&D managers express displeasure with the state of the art in portfolio decision models and are often doubtful about their firm's current portfolio of projects. Many feel that conventional portfolio decision models are impractical, requiring data that are almost impossible to estimate, or fail to take into account the risk-mitigating effects of diversification across a portfolio. The new approach presented here is scientifically rigorous, yet simple and practical. It can be implemented easily using only an EXCEL spreadsheet, and requires as inputs only the estimated success probabilities and payoffs of each R&D project. The model develops priorities for each of the R&D projects, which fake into account the joint risk of the entire portfolio. The project priorities developed by this model are closely correlated with the internally established priorities for 54 R&D projects in two large ethical pharmaceutical firms.