Supply Chain Risk Diffusion in Partially Mapping Double-Layer Hypernetworks.

Area of Science:

• Supply Chain Management
• Network Science
• Epidemiology

Background:

• COVID-19's global impact creates uncertainty, affecting supply chain product quality and worker efficiency.
• Complex supply chain networks are vulnerable to risk diffusion driven by uncertain information.

Purpose of the Study:

• To model and analyze supply chain risk diffusion under uncertain information using a double-layer hypernetwork.
• To investigate the effectiveness of different node removal strategies on market stability and risk spread.

Main Methods:

• Developed a partial mapping double-layer hypernetwork model incorporating individual heterogeneity.
• Utilized an Susceptible-Potential-Infected-Recovered (SPIR) model, drawing from epidemiology, to simulate risk diffusion.
• Employed the microscopic Markov chain approach (MMCA) for theoretical validation and Matlab simulations.

Main Results:

• Eliminating aging (outdated) enterprises is more effective for market stability than controlling key enterprises during risk diffusion.
• Increasing upper-layer mapping (authoritative information dissemination) reduces the number of infected enterprises.
• Decreasing lower-layer mapping reduces misled enterprises, thereby weakening risk infection efficiency.

Conclusions:

• The study highlights the critical role of information dissemination and network structure in supply chain risk management.
• Strategies focusing on removing outdated enterprises and managing information flow are crucial for mitigating risk diffusion.
• The developed model provides valuable insights for enhancing supply chain resilience and stability in uncertain environments.