Resilient pharmaceutical supply chains: Assessment of stochastic optimization strategies for process uncertainty integration in network design problems

Abstract

In recent years, the market boom of next-generation therapies and vaccines has pressured the pharmaceutical industry to rapidly scale up capacity to meet societal needs. Manufacturers catering for these markets reported shortages due to unforeseen demand trends and a crucial uncertainty in capabilities of platforms still under development. In this work, we present an optimization-simulation framework for the design of resilient supply chains to manufacturing uncertainty. Given previously quantified probability distributions of process parameters, we formulate stochastic optimization problems integrating process uncertainty via a sampling-based methodology. Stochastic programming results in networks of higher optimal costs compared to deterministic approaches. Furthermore, stochastic designs ensure product supply meets target demands under simulated uncertainty and result in a larger probability of achieving lower costs per dose. The optimization-simulation framework is used to test solution stability for a varying number of optimization scenarios, highlighting that the minimum number of samples to guarantee stability is problem-specific, thus motivating the investigation of scenario reduction techniques to ensure stability of scenario sets a priori. Overall, the cost-supply benefits of integrating manufacturing uncertainty are quantified, demonstrating the scope for its consideration in strategic planning problems in the sector.