Improved Acquisition for System Sustainment: Multi-sourcing Resilient Supplier Selection Under Stochastic Disruptions

Abstract

Recognizing the inevitability of large-scale disruptions, emphasis in supply chain decision making has shifted from prevention and protection to resilience, or the ability to withstand, adapt to, and recover in a timely manner from a disruptive event. Recent work in supply chain resilience has primarily consisted of qualitative frameworks and lessons learned after disruptions. This project addresses resilient supplier selection, a significant concern across industry and government enterprises. This work develops a supplier selection decision framework that includes (i) a multi-objective optimization formulation for multi-sourced supplier selection and (ii) a means to address uncertainty underlying the occurrence of a disruptive event through a Bayesian network-driven measure of disruption likelihood. The model accounts for several resilience strategies such as increasing supplier capacity beyond normal levels is a mitigation strategy to fortify suppliers against disruption, connecting firms to a back-up supplier as a contingency strategy that allows the supplier to adapt its loss by reconfiguring the channels for the movement of materials, and a contingency strategy of having additional recovery resources to enable suppliers to restore lost capacity more quickly.