LAUSR

Abstract Resilient supplier selection is a key strategic decision in the context of the supply chain (SC) disruption management. We offer an efficient solution to the resilient supplier selection and optimal order allocation problem. We first show how to compute the likelihood of disruption scenarios for the supplier selection problem using a probabilistic graphical model. That model can capture (i) a large number of disruptive events with no computational burden, and (ii) the dependencies among disruptive events and their impacts on supplier performance, i.e., the ripple effect. We then propose a stochastic bi-objective mixed integer programming model to support the decision-making in how and when to use both proactive and reactive strategies in supplier selection and order allocation. The outcomes of this research, if utilized properly, can benefit suppliers to find the optimal set of operational decisions (e.g., the optimal level of surplus capacity and restorative capacity) that enhance their resilience capabilities. Finally, the proposed model can be utilized as a decision support tool to assist manufacturers in performance assessment of supplier alternatives when costs and resilience are considered simultaneously, which helps to build up both efficient and resilient SC (i.e., to achieve the SC resilience) to ensure the operations continuity. These outcomes can help SC managers organize their disruption risk mitigation efforts with balancing the efficiency and resilience while focusing on critical suppliers and order (re)-allocation that will have a more significant impact on the performance of the SC when disrupted.