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导致了近年来最严重的供应链中断事件。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事件产生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，真实刻画氧气浓缩器（OC）的需求模式。基于这一精细化的需求信号，我们进一步构建了涵盖OC采购、制造与配送的供应链离散事件仿真模型，用于测试满足激增需求的生产扩张策略。该模型整合了公开数据集、OC工程拆解分析及供应链全景映射技术以识别供应商。研究结果表明，这种耦合方法能够利用突发事件中的真实需求，快速提出兼顾成本控制的供应链韧性增强策略建议。