Enhancing Supply Chain Resilience through Network Characteristics

초록

Purpose: This study explores how fundamental structural characteristics of supply chains?such as centrality measures, clustering coefficients, and alternative resource paths?impact sup-ply chain resilience (SCR) and quality management. Unlike previous research that relies on predefined network typologies (e.g., scale-free, small-world), this study shifts the focus to dynamic, multi- layered network structures to enhance SCR, improve supply chain quality, and strengthen quality management practices. Methods: The study integrates Bayesian networks with an agent-based modeling (ABM) simulation framework to assess disruption propagation and recovery processes in supply chains. Bayesian networks are used to capture structural dependencies and probabilistic interactions among network components, while ABM simulates how disruptions spread and how the system recovers. This novel methodological approach provides insights into the role of key nodes, inter-layer interactions, and alternative risk propagation paths. Findings: The research reveals that analyzing core structural properties of supply chain net-works, rather than relying on predefined network typologies, leads to a deeper understanding of SCR. The integration of Bayesian networks and ABM allows for adaptive, data-driven net-work metrics that improve resilience. These findings highlight the importance of key nodes, flexible resource paths, and dynamic interactions in mitigating disruptions and enhancing overall supply chain quality. Practical Implication: The study provides actionable insights for supply chain managers by demonstrating how structural characteristics can be leveraged to improve resilience and quality management. Originality/Value of Paper: This study advances SCR research by moving beyond rigid network typologies and introducing a novel integrated approach using Bayesian networks and ABM.