Supply Chain Flexibility to Unforeseen Demand Under Quantified Process Uncertainty: A Pharmaceutical Case Study

ABSTRACT As supply chain resilience becomes a growing priority across industries, there emerges a need for decision support tools for advanced manufacturing planning under uncertainty. The pharmaceutical industry is a representative case, with manufacturers catering for emerging gene therapy and vaccine applications reporting delays and shortages in recent years due to the unforeseen pandemic, the need to rapidly re‐purpose manufacturing resources, combined with uncertain process performance of established capacity. Whilst process uncertainty is often quantifiable, tackling unforeseen demands requires the establishment of flexible production platforms. To this end, we present a framework for the quantification of network design flexibility integrating quantified process uncertainty. Network reliability metrics are quantified through scenario‐based chance constraint programming and solution quality is tested via Monte Carlo simulation. Cost‐reliability plots are obtained to pinpoint the required costs and capacity to meet a target probability of product demand satisfaction. Given an upper bound in cost and fixed design, the tool is also used to map out a feasible solutions space, quantifying maximum network reliability. Improving network reliability with this proactive approach supports supply chain resilience, as the impact of unexpected events can be understood and mitigated a priori.