An Integrated System Dynamics and Discrete Event Supply Chain Simulation Framework for Supply Chain Resilience with Non-Stationary Pandemic Demand

TL;DR

This paper presents a hybrid simulation framework combining system dynamics and discrete event modeling to improve supply chain resilience during non-stationary demand shocks like pandemics, demonstrated through oxygen concentrator supply chains.

Contribution

It introduces an integrated simulation approach coupling epidemiological demand modeling with supply chain simulation, enabling rapid policy testing during disruptions.

Findings

Coupled model accurately predicts demand surges during COVID-19.

Simulation identifies effective supply augmentation policies.

Framework supports real-time decision-making for resilient supply chains.

Abstract

COVID-19 resulted in some of the largest supply chain disruptions in recent history. To mitigate the impact of future disruptions, we propose an integrated hybrid simulation framework to couple nonstationary demand signals from an event like COVID-19 with a model of an end-to-end supply chain. We first create a system dynamics susceptible-infected-recovered (SIR) model, augmenting a classic epidemiological model to create a realistic portrayal of demand patterns for oxygen concentrators (OC). Informed by this granular demand signal, we then create a supply chain discrete event simulation model of OC sourcing, manufacturing, and distribution to test production augmentation policies to satisfy this increased demand. This model utilizes publicly available data, engineering teardowns of OCs, and a supply chain illumination to identify suppliers. Our findings indicate that this coupled approach can use realistic demand during a disruptive event to enable rapid recommendations of policies for increased supply chain resilience with controlled cost.