Risk propagation in supply-chain network considering node heterogeneity

Abstract

In the current highly interconnected global economy, risk propagation within supply-chain networks has drawn significant attention from researchers because of its profound impact. Given the varying risk-propagation capabilities of different firms within the supply chain, we propose a risk-propagation model that considers the heterogeneity between nodes, referred to as the Degree-Dependent Risk Propagation (DDRP) model. We analyze the effects of different heterogeneity parameters on the performance of risk propagation in the supply-chain network and further explore how these effects influence the efficiency of logistics within the supply-chain network. The results indicate that the heterogeneity between nodes significantly increases the vulnerability of the supply-chain network, making it less efficient when facing risk propagation. In a highly heterogeneous network, more nodes become infected, leading to a notable decline in logistics-transportation efficiency, which severely disrupts the normal functioning of the entire supply chain. Our research not only provides a novel theoretical model for risk propagation in supply-chain networks, but also offers valuable practical insights for managers and decision-makers. By identifying and understanding the influence of heterogeneity on risk propagation, decision-makers can formulate more effective risk-management strategies, thereby enhancing supply-chain resilience and efficiency.