Network-position heterogeneity and nonlinear risk propagation in supply chain networks under external shocks

Amid the increasing complexity and uncertainty of global supply chains, external shocks have become a major source of systemic risk in supply chain networks. Existing studies are largely based on homogeneous assumptions and single-system frameworks, making it difficult to reveal the coupling interactions among firms with different network positions and their effects on the dynamic evolution of risk propagation. To address this gap, this study is positioned as a theoretical modeling and numerical simulation study. From the perspective of network-position heterogeneity among supply chain firms, it integrates propagation dynamics with discrete dynamics to construct a heterogeneous, dual-population coupled spatiotemporal model of risk propagation. The model is used to investigate the nonlinear evolution mechanisms of shock-driven risk propagation in supply chains. The results show that: (1) The basic reproduction capacity of systemic risk is jointly determined by the risk propagation reproduction capacities of high-network-position firms and ordinary firms. In addition, the stronger the business coupling with high-network-position firms, the more pronounced the network risk propagation effect.(2) When the number of core firms is relatively large and the business dependence between core firms and ordinary firms is strong, local risks are more likely to spread through supply chain relationships and evolve into systemic risks. The more concentrated the initially infected firms are, the more likely risks are to form spatial clusters in local areas and further diffuse to surrounding firms.(3) The timing of intervention plays a decisive role in risk control effectiveness. The optimal control strategy constructed in this study can suppress risk propagation in the network at the early stage and exhibits stronger risk suppression capability in high-risk network structures characterized by core-firm agglomeration and close business connections. However, when the initial risk sources are highly concentrated, risks possess stronger initial diffusion potential, which substantially increases the difficulty of risk control. This study reveals the spatiotemporal evolution mechanism of supply chain risk propagation driven by network-position heterogeneity and coupling effects, enriches the dynamical theory of systemic risk propagation in complex networks, and provides a new theoretical perspective for studying risk propagation and system stability in heterogeneous complex systems.