Risk Propagation and Supply Chain Health Control Based on the SIR Epidemic Model

• Published in: Mathematics (Basel) 2022-08, Vol.10 (16), p.3008
• Main Authors: Liang, Di, Bhamra, Ran, Liu, Zhongyi, Pan, Yucheng
• Format: Article
• Language: English
• Subjects: Automobile industry; Complex systems; Control; Coronaviruses; COVID-19; Disease transmission; Earthquakes; Epidemics; Epidemiology; Factories; Health aspects; Health risk assessment; Infectious diseases; Mathematical models; Mathematical research; Network analysis; Pandemics; Propagation; Risk management; risk propagation; SIR epidemic model; Suppliers; supply chain resilience; supply chain risk management; Supply chains; supply network health
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10163008

Risk propagation is occurring as an exceptional challenge to supply chain management. Identifying which supplier has the greater possibility of interruptions is pivotal for managing the occurrence of these risks, which have a significant impact on the supply chain. Identifying and predicting how these risks propagate and understanding how these risks dynamically diffuse if control strategies are installed can help to better manage supply chain risks. Drawing on the complex systems and epidemiological literature, we research the impact of the global supply network structure on risk propagation and supply network health. The SIR model is used to dynamically identify and predict the risk status of the supply chain risk at different times. The results show that there is a significant relationship between network structure and risk propagation and supply network health. We demonstrate the importance of supply network visibility and of the extraction of the information of node firms. We build up an R package for geometric graphs and epidemics. This paper applies the R package to model the supply chain risk for an automotive manufacturing company. The R package provides a firm to construct the complicated interactions among suppliers and display how these interactions impact on risks. Theoretically, our study adapts a computational approach to contribute to the understanding of risk management and supply networks. Managerially, our study demonstrates how the supply chain network analysis approach can benefit the managers by developing a more holistic framework of system-wide risk propagation. This provides guidance for network governance policies, which will lead to healthier supply chains.