Reputation-Based Evidential Reasoning Approach Improves Cooperative Evolution of Supply Chain Network Under Disruption Risk

Abstract

In the supply chain network frequently facing disruption risk, sustaining a high reputation is essential for ensuring long-term sustainability. While existing studies have investigated how reputation evidence influences the overall network structure and cooperative behaviors, they have not extensively examined how variations in reputation evidence affect cooperative dynamics in the bipartite graph consisting of upstream and downstream enterprises. To bridge this gap, our research introduces a novel, reputation-based approach utilizing evidential reasoning theory to assess dynamic environments prone to disruption risk. This improved approach assigns specific weights and reliability to reputation evidence. In this article, we analyze the evolution of cooperation between upstream and downstream enterprises in the supply chain, considering varied distributions of reputation evidence. We also conduct numerical simulations to evaluate the impact of these reputation distributions on the cooperative evolution of the supply chain network under diverse disruption scenarios, including both direct and indirect risks. Our findings offer insightful guidance for managers to refine their reputation distribution strategies, effectively navigate different disruption risk scenarios, and lessen negative impacts on cooperation. Moreover, our study provides significant implications for partner selection and managing disruption risk in a dynamic and uncertain supply chain network.