A framework for addressing stochastic and combinatorial aspects of scheduling and resource allocation in pharmaceutical R&D pipelines

Managing a pharmaceutical R&D pipeline is a complex undertaking that involves an inter-play of strategic and tactical decision-making around portfolio selection, activity scheduling and resource allocation across multiple projects along a multi-year scope. In this paper we focus on improving the quality of pharmaceutical resource management decisions and practices. Pharmaceutical resource management is broadly comprised of decisions relating to scheduling and allocation of limited resources across development activities of multiple drug projects that are subject to technological and market uncertainties, long development cycle times as well as work process constraints. These complexities only magnify the risk of sub-optimal resource management. Notwithstanding this risk, the literature on joint optimization of scheduling and resource allocation decisions in the context of pharmaceutical R&D pipelines is limited. In this paper, a framework—SIM-OPT is proposed as an integrated resource management tool with the goals of maximizing the portfolio's expected net present value (ENPV), controlling risk and reducing drug development cycle times. The framework includes three key components: (1) a stochastic simulation of the pharmaceutical work flow process, (2) a ‘resource manager’ based on a mixed integer linear programming formulation that schedules and allocates resources as a function of demands from the simulated work process and (3) a ‘strategy learner’ that evaluates the impact of various resource strategies on the financial and cycle time performance of the simulated pipeline and draws key learnings. The output is a recommended set of resource management strategies and their impacts on expected return, risk and cycle time metrics. The effectiveness of the framework is demonstrated via its application to two industrial case studies drawn from a major pharmaceutical corporation. This approach offers potential benefits in terms of its ability to transform key learnings into efficient and reliable resource management practices that are well aligned with the overall business strategy.