Inventory Optimization and Simulation Analysis for Supply Chain Disruption Events

Abstract

Increasing volatility in the global supply chain following the Covid-19 pandemic has led to a challenge in reliably managing inventory, especially for high-complexity medical devices. An optimization and simulation-based inventory management model was developed to augment the decision making of supply planners in these networks. The model supports supply planners in safety stock allocation decisions by quantifying inventory cost and stockout probability risk for products with multi-stage, converging supply networks. Components of the model include iterative multi-echelon inventory optimization, monte carlo simulation of a custom base-stock inventory model and cycle service level modelling. An application of the model is explored in a case study of the J&J Ethicon surgical stapler supply chain. In addition, operational considerations for implementing inventory models are discussed, including data architecture, standardization, and centralization for complex supply chains.