ABSTRACT
The recent COVID-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience. © 2022 Elsevier Ltd