A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic

• Published in: International journal of production economics Vol. 263; p. 108935
• Main Authors: Brusset, Xavier, Ivanov, Dmitry, Jebali, Aida, La Torre, Davide, Repetto, Marco
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.09.2023
• Subjects: Control; Optimal control; Ripple effect; Risk management; Supply chain adaptation; Ripple effect; Control; Risk management; Supply chain adaptation; Optimal control
• ISSN: 0925-5273; 1873-7579
• DOI: 10.1016/j.ijpe.2023.108935

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. •The model is a novel approach in supply chain risk management.•A combination of epidemiological and optimal control model improves supply chain viability.•This prescriptive model provides a manager with the optimal subset of interchangeable suppliers over a planning horizon.•The supply chain ripple effect is reduced.•Three illustrations are discussed: increasing, decreasing, and mixed infection in suppliers.