Supply chain network viability: Managing disruption risk via dynamic data and interaction models

Abstract

This study addresses the challenge of enhancing viability of an interconnected supply chain network, particularly in the context of low-probability high-impact events that recur unpredictably. We re-examine the viability from the views of agility, resilience, and sustainability, and propose a novel hybrid approach which integrates dynamic network data and multi-echelon interaction. Diverging from traditional static approaches, we introduce a dynamic decision-making framework that strategically maintains long-term survival by coordination between timely response actions and the risk of overreaction. A data-driven hidden Markov model is built to update the risk forecasting via dynamic network data. A Bayesian network game theoretical model is developed to support collaborative risk mitigating via the multi-echelon interaction. The main findings are as follows. In the short term, we encourage enterprises to engage in collaborative risk mitigating to significantly increase the likelihood of reaching a consensus on achieving a more cost-efficient level of risk mitigation, marked by an intriguing interplay between weakened individual fairness and the tendency to mitigate network-wide risk more economically. In the long term, we advocate building a data-driven, structure-dynamic, and interaction-focused risk response timing system to enable the network to adapt to changes swiftly and achieve desired viable levels efficiently.