A Stochastic Model for Pharmaceutical R&D Project Management in a Make-or-Buy Decision Setting

Managing a pharmaceutical R&D project is a complex undertaking that involves efforts from both business managers and scientists. The increasing complexity of exploratory activities in pharmaceutical innovation makes less likely that a project can stand alone. Project managers not only resort to in-house innovation but also external sources to propel a central project. In this paper, the authors propose a make-or-buy stochastic process model as an integrated project management tool with the goal of maximizing the successful probability of a prospective drug compound. The model illustrates the two-process-line practice in pharmaceutical R&D projects and combines this practice with the make-or-buy decision that managers always face in pharmaceuticals. Using this model, the authors discuss the decision strategies at different phases of a pharmaceutical R&D project and provide optimal solutions based on the remaining time of the project. A case study demonstrates the model's effectiveness. The model offers potential benefits in terms of its ability to transform key learning into efficient and reliable managerial decision-making practices that are well aligned with drug innovation strategies.