An Integrated Supply Chain Risk Mitigation tool - Model, Analysis, and Insights

Abstract In this paper, we propose a two-phase approach that seeks to create insights into disruptions, allowing managers to mitigate supply chain risks using an integrated risk-mitigation tool. In the first phase, the problem is formulated as a Markov Decision Process (MDP) that maximizes the expected long-run revenue induced by supply chain risks, given the current state of the risk score. We map four decision states to four specific managerial actions: "do nothing," "track the supplier," "monitor the supplier," and "change the supplier." If the optimal policy is "do nothing", then the supply chain risk scores are continued to be monitored. In the case of "track the supplier", the firm will track the supplier’s performance internally, however, in the case of "monitor the supplier", the firm will hire an external contractor to monitor the external supply chain risks in the supplier’s country and macroeconomic factors. Lastly, if the optimal policy from phase 1 is to ’change the supplier,’ the best-performing supplier is identified using an integrated best-worst goal programming (BWGP) method as a multi-criteria decision-making (MCDM) method. This second phase of decision support can help allocate supply contracts to the least risky supplier. We demonstrate the integrated risk-based model and the proposed best-worst goal programming (BWGP) method on a global automation technologies company and share the risk-mitigating capability as a case study. Results based on consistency ratio, conformity, and total deviation show that the BWGP performs better than the other MCDM methods and can be used effectively in the integrated two-phase approach with MDP as a supply chain risk mitigation tool.