Capacity Planning Under Uncertainty for the Pharmaceutical Industry

Abstract

Market pressures and tough socio-political regulations are among the current factors that are changing the way in which the pharmaceutical business is operated. The pharmaceutical industries are faced with the question of the best use of the limited resources available to obtain the highest possible profit from a potential product portfolio. Thus, they are being forced to consider ever more systematic approaches to optimize their potential product portfolio. Here, we present a mathematical programming approach for the problem of capacity planning under clinical trials uncertainty. This optimization-based approach selects the final product portfolio and the production planning and investment strategy simultaneously subject to the uncertainty of the outcomes of the clinical trials for each potential drug. Four clinical trial outcomes (high success, target success, low success, failure) for each product are considered as is typical in the industry. As these outcomes have different probabilities of occurrence and the information from the trials will become available at different times, the investment problem becomes a large-scale, multistage, multiperiod stochastic optimization problem, which is then reformulated as a multiscenario, mixed integer linear programming (MILP) model. For this model, a performance measure that takes appropriate account of risk and potential returns has also been formulated. The applicability of the model is demonstrated by an illustrative example.