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

COVID-19 resulted in some of the largest supply chain disruptions in recent history. To mitigate the impact of future disruptions, an integrated hybrid simulation framework is proposed to couple nonstationary demand signals from an event like COVID-19 with a model of an end-to-end supply chain. First, a system dynamics susceptible-infected-recovered (SIR) model is created, augmenting a classic epidemiological model to create a realistic portrayal of demand patterns for oxygen concentrators (OC). Informed by this granular demand signal, a supply chain discrete event simulation model of OC sourcing, manufacturing, and distribution is developed 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. The 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.