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.

翻译：COVID-19引发了近年来最严重的供应链中断。为减轻未来中断的影响，我们提出了一种集成混合仿真框架，将类似COVID-19事件中的非平稳需求信号与端到端供应链模型相耦合。首先构建系统动力学易感-感染-康复（SIR）模型，通过增强经典流行病学模型，真实刻画制氧机（OC）的需求模式。基于这一精细化的需求信号，我们进一步创建包含OC采购、制造和分销的供应链离散事件仿真模型，以测试满足激增需求的产能提升策略。该模型利用公开数据、OC工程拆解报告以及供应链透明化分析识别供应商。研究结果表明，这种耦合方法能够利用干扰事件中的实际需求，快速提出控成本下的供应链韧性提升政策建议。