Brusset Xavier, Ivanov Dmitry, Jebali Aida, La Torre Davide, Repetto Marco
SKEMA Business School, Université Côte d'Azur, Paris, France.
Berlin School of Economics and Law, Berlin, Germany.
Int J Prod Econ. 2023 Sep;263:108935. doi: 10.1016/j.ijpe.2023.108935. Epub 2023 Jun 10.
The COVID-19 pandemic has illustrated the unprecedented challenges of ensuring the continuity of operations in a supply chain as suppliers' and their suppliers stop producing due the spread of infection, leading to a degradation of downstream customer service levels in a ripple effect. In this paper, we contextualize a dynamic approach and propose an optimal control model for supply chain reconfiguration and ripple effect analysis integrated with an epidemic dynamics model. We provide supply chain managers with the optimal choice over a planning horizon among subsets of interchangeable suppliers and corresponding orders; this will maximize demand satisfaction given their prices, lead times, exposure to infection, and upstream suppliers' risk exposure. Numerical illustrations show that our prescriptive forward-looking model can help reconfigure a supply chain and mitigate the ripple effect due to reduced production because of suppliers' infected workers. A risk aversion factor incorporates a measure of supplier risk exposure at the upstream echelons. We examine three scenarios: (a) infection limits the capacity of suppliers, (b) the pandemic recedes but not at the same pace for all suppliers, and (c) infection waves affect the capacity of some suppliers, while others are in a recovery phase. We illustrate through a case study how our model can be immediately deployed in manufacturing or retail supply chains since the data are readily accessible from suppliers and health authorities. This work opens new avenues for prescriptive models in operations management and the study of viable supply chains by combining optimal control and epidemiological models.
新冠疫情凸显了确保供应链运营连续性面临的前所未有的挑战，因为供应商及其供应商因感染传播而停止生产，进而以连锁反应的方式导致下游客户服务水平下降。在本文中，我们将一种动态方法置于具体情境中，并提出了一个用于供应链重新配置和连锁反应分析的最优控制模型，该模型与疫情动态模型相结合。我们为供应链管理者提供在规划期内从可互换供应商子集和相应订单中进行的最优选择；鉴于其价格、交货时间、感染风险以及上游供应商的风险暴露，这将使需求满意度最大化。数值示例表明，我们的规范性前瞻性模型能够帮助重新配置供应链，并减轻因供应商工人感染导致生产减少所引发的连锁反应。一个风险规避因素纳入了对上游层级供应商风险暴露的衡量。我们考察了三种情形：(a) 感染限制了供应商的产能；(b) 疫情消退，但所有供应商的消退速度不同；(c) 感染浪潮影响一些供应商的产能，而其他供应商处于恢复期。我们通过一个案例研究说明了我们的模型如何能够立即应用于制造或零售供应链，因为数据可从供应商和卫生当局轻松获取。这项工作通过结合最优控制和流行病学模型，为运营管理中的规范性模型以及可行供应链的研究开辟了新途径。
