A Risk Identification and Assessment of Cross-border Supply Chain Disruption Based on Bayesian Networks

Abstract

Cross-border supply chains have become increasingly complex and critical in today's interconnected global economy. However, effective risk management in this context remains a significant challenge. The study analyzes and quantifies the impact of different factors on cross-border supply chains by establishing a Bayesian network model and identifies and evaluates interruption risks based on causal reasoning and diagnostic reasoning techniques. The results show that the combination of Bayesian networks can comprehensively reveal the causal relationships and influences of interruption risks in cross-border supply chains, providing theoretical support for risk control implementation in cross-border supply chain business for banks and other enterprises. This study also proposes control strategies for interruption risks in cross-border supply chains, including strengthening information construction, formulating emergency plans and recovery mechanisms, etc., to reduce the losses caused by interruption risks and ensure the stable operation of the supply chain.