Managing Supply Networks in Regulated Markets: three essays using simulation

Abstract

We present a comprehensive overview of three research topics related to supply chain management. The first essay investigates the impact of a complexity reduction project conducted in the life science industry to improve financial performance. The essay presents the complex infrastructure of antibody manufacturing, distribution and the benefits of applying discrete event simulation to reduce complexity. The digital twin model allows us to evaluate multiple scenarios to support decision-making when redesigning a supply chain. Based on our analysis, we identify significant improvements. The second essay explores how supply chain risks can be identified in a dense supply network to mitigate the impact of disruptions. We developed a novel approach to help companies identify critical nodes in their supply network and apply risk mitigation strategies to reduce risk across the network. Our research shows that the combination of Social Network Analysis (SNA), network graph visualisation, and Monte Carlo Simulation (MCS) improves the ability of decision makers to identify and manage risks. Optimal inventory policies are crucial to supply chain management, especially when dealing with stochastic demand and lead time. The third essay explores the optimal inventory policies under these conditions. First, the fundamental concepts and models of inventory management and policies are discussed. The essay then delves into the complexities of managing inventory with stochastic demand and lead time, exploring four optimal inventory policies to minimise total inventory costs utilising a simulation optimisation approach. The essay concludes with a sensitivity analysis, and we present the four optimal inventory policies for different service levels.