PDF(Pubmed)
Abstract：
COVID-19 has posed unprecedented challenges to global health and the world economy. Two years into the pandemic, the widespread impact of COVID-19 continues to deepen, impacting different industries such as the automotive industry and its supply chain. This study presents a hybrid approach combining simulation modeling and tree-based supervised machine learning techniques to explore the implications of end-market demand disruptions. Specifically, we apply the concept of born-again tree ensembles, which are powerful and, at the same time, easily interpretable classifiers, to the case of the semiconductor industry. First, we show how to use born-again tree ensembles to explore data generated by a supply chain simulation model. To this end, we demonstrate the influence of varying behavioral and structural parameters and show the impact of their variation on specific key performance indicators, e.g., the inventory level. Finally, we leverage a counterfactual analysis to identify detailed managerial insights for semiconductor companies to mitigate adverse impacts on one echelon or the entire supply chain. Our hybrid approach provides a simulation model enhanced by a tree-based supervised machine learning model that companies can use to determine optimal measures for mitigating the adverse effects of end-market demand disruptions. We close the loop of our analysis by integrating the findings of the counterfactual analysis backward into the simulation model to understand the overall dynamics within the multi-echelon supply chain.
摘要：
COVID-19对全球卫生和世界经济构成了前所未有的挑战。大流行两年后，COVID-19的广泛影响继续加深，影响汽车行业及其供应链等不同行业。本研究提出了一种结合仿真建模和基于树的监督机器学习技术的混合方法，以探索终端市场需求中断的影响。具体来说,我们应用重生树合奏的概念，它们很强大，同时,易于解释的分类器，以半导体行业为例。首先,我们展示了如何使用重生树集合来探索由供应链仿真模型生成的数据。为此,我们展示了不同的行为和结构参数的影响，并显示了它们的变化对特定关键绩效指标的影响，例如，库存水平。最后,我们利用反事实分析来确定半导体公司的详细管理见解，以减轻对一个梯队或整个供应链的不利影响。我们的混合方法提供了一个仿真模型，该模型由基于树的监督机器学习模型增强，公司可以使用该模型来确定最佳措施，以减轻终端市场需求中断的不利影响。我们通过将反事实分析的结果向后集成到仿真模型中，以了解多级供应链中的整体动态，从而关闭了分析的循环。
