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Risk transmission in complex supply chain network with multi-drivers

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  • Wang, Jiepeng
  • Zhou, Hong
  • Jin, Xiaodan

Abstract

Using epidemic model to study supply chain risk transmission, multi-drivers including the enterprise risk preference, the operational robustness and flexibility, the completeness of market information and especially the network topology are explored. The mechanism and evolution of the complex supply chain network risk transmission are discussed. We find that (1) For a complex supply chain network, when the risk transmission is lower than the threshold, the risk tends to die, and when the risk transmission is greater than the threshold, the risk trends to achieve transmission and diffusion, and tends to a non-zero equilibrium point. (2) Risk transmission threshold is positively correlated with the operational robustness and flexibility, the completeness of market information, and immunity rate; while it is negatively correlated with the enterprise risk preference. (3) Risk transmission scale is negatively correlated with the operational robustness and flexibility, the completeness of market information, and immunity rate; while it is positively correlated with the enterprise risk preference. (4) The heterogeneity of network is greater, the risk transmission threshold is greater, the risk transmission scale is lower. The conclusion has important theoretical and practical significance for supply chain risk management.

Suggested Citation

  • Wang, Jiepeng & Zhou, Hong & Jin, Xiaodan, 2021. "Risk transmission in complex supply chain network with multi-drivers," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s096007792030655x
    DOI: 10.1016/j.chaos.2020.110259
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    1. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    2. Sterman, John D., 1989. "Misperceptions of feedback in dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(3), pages 301-335, June.
    3. Tatsuya Hirano & Yasushi Narushima, 2019. "Robust Supply Chain Network Equilibrium Model," Transportation Science, INFORMS, vol. 53(4), pages 1196-1212, July.
    4. Nikolay Osadchiy & Vishal Gaur & Sridhar Seshadri, 2016. "Systematic Risk in Supply Chain Networks," Management Science, INFORMS, vol. 62(6), pages 1755-1777, June.
    5. Yao, Z. & Leung, Stephen C.H. & Lai, K.K., 2008. "Manufacturer's revenue-sharing contract and retail competition," European Journal of Operational Research, Elsevier, vol. 186(2), pages 637-651, April.
    6. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
    7. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    8. Jiri Chod & David Pyke & Nils Rudi, 2010. "The Value of Flexibility in Make-to-Order Systems: The Effect of Demand Correlation," Operations Research, INFORMS, vol. 58(4-part-1), pages 834-848, August.
    9. Charles H. Fine & Robert M. Freund, 1990. "Optimal Investment in Product-Flexible Manufacturing Capacity," Management Science, INFORMS, vol. 36(4), pages 449-466, April.
    10. Wang, Hua & Gu, Tao & Jin, Maozhu & Zhao, Rong & Wang, Guanxiang, 2018. "The complexity measurement and evolution analysis of supply chain network under disruption risks," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 72-78.
    11. Chen, Tingqiang & Wang, Jiepeng & Liu, Haifei & He, Yuanping, 2019. "Contagion model on counterparty credit risk in the CRT market by considering the heterogeneity of counterparties and preferential-random mixing attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 458-480.
    12. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.
    13. Yue Dong & Jiepeng Wang & Tingqiang Chen, 2019. "Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer-Coupled Networks," Complexity, Hindawi, vol. 2019, pages 1-21, April.
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    2. Berger, Niklas & Schulze-Schwering, Stefan & Long, Elisa & Spinler, Stefan, 2023. "Risk management of supply chain disruptions: An epidemic modeling approach," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1036-1051.
    3. Wang, Jiepeng & Zhou, Hong & Sun, Xinlei & Yuan, Yufei, 2023. "A novel supply chain network evolving model under random and targeted disruptions," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
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    6. Wang, Jiepeng & Zhou, Hong & Zhao, Yujie, 2022. "Behavior evolution of supply chain networks under disruption risk — From aspects of time dynamic and spatial feature," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    7. Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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