Supply chain network strategy for consumer medical device introduction

Abstract

This thesis presents an optimization framework to model the trade-offs in strategic supply chain decision-making for a new product introduction in a real-world setting. The focus of the thesis is on a consumer medical device that Johnson & Johnson's Calibra business will launch in the future. As with any new product introduction, the launch exposes the J&J business to risk and uncertainty. We develop a mixed-integer optimization model to guide the optimal design of a global consumer medical device supply chain network comprising component suppliers, assembly facilities, sterilizers, and distribution centers. The model evaluates strategic decisions over a seven-year time horizon related to the location and capacities of various supply chain facilities and partners, transportation costs, and strategic inventory required to satisfy global demand. We developed a stochastic optimization extension of the model to protect the supply chain decision maker from demand uncertainty. Comparison of the output of the model assuming deterministic demand to a managerial heuristic resulted in total supply chain network cost reductions of 19% - 27%, amounting to hundreds of millions in present-value dollars. The stochastic optimization solution reduces infeasibility related to either not meeting the demand or transportation lead time constraints. The two models presented in this thesis enable J&J supply chain decision makers to gauge the additional costs and benefits of different network design concepts, develop a network strategy that can adapt to uncertain demand, and create a strong strategic foundation for future tactical and operational decisions.