Research on Supply Chain Network Resilience Considering the Exit and Reselection of Enterprises

• Main Authors: Yongzheng Tian, Yuqiang Shi, Xiaoqiu Shi, Minghui Li, Min Zhang
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Complex networks; disruptions; network structures; supply chain network resilience
• Online Access: https://ieeexplore.ieee.org/document/9458246/

In a complex environment, how to construct a resilient supply chain network (SCN) that can resist disruptions is a problem of great concern in supply chain management. In order to provide some insights for constructing a resilient SCN, this paper studies the influence of network structures on SCN resilience from the perspective of complex networks. Considering the exit and reselection of enterprises that have been ignored in previous studies, we propose a new SCN model in which nodes are connected with each other based on degree, fitness and distance. Subsequently, different disruption scenarios are simulated and the resilience of the SCN generated by the proposed model is compared with that of previous models. The simulation results show that the SCN generated by the proposed model is resilient to random disruptions, but vulnerable to targeted disruptions. In particular, the resilience of the SCN will be seriously affected when the strong-strong alliance is broken. Through the research on the influence of the parameters (e.g., <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>) of the proposed model on SCN resilience, we find that <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> has a significant impact on SCN resilience. The larger the value of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is, the worse the resilience of the corresponding SCN is. The parameter <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> has a slight effect on SCN resilience, and the more uniform the distribution of <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> in the network is, the better the resilience of the corresponding SCN is. This study may contribute to the design and resilience optimization of SCNs.