Impact of heterogeneity on risk propagation in supply chain networks

In the highly interconnected global economy, risk propagation in supply chain networks has garnered significant attention due to its profound impact. Based on complex network theory, this paper proposes a Degree-Dependent Heterogeneous Risk Propagation (DDHRP) model, in which risk is represented as a binary state within a susceptible–infected–susceptible (SIS) framework: a firm is either infected or susceptible. By use of mean-field equations, we derive the risk propagation threshold and the infection probability under varying basic propagation rates ${\lambda }_0$, and analyze how different heterogeneity parameters affect the distribution of infection probability across degrees of nodes. Experimental simulations show that the monotonicity of a node’s infection probability with respect to node degree can be controlled by adjusting model parameters. Moreover, increasing heterogeneity in risk transmission reduces the average infection prevalence across nodes, whereas greater heterogeneity in risk recovery increases it. Our research not only offers a heterogeneous risk propagation model adapted for supply chain networks, but also provides a theoretical foundation for risk early warning and control.