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Research on supply network resilience considering random and targeted disruptions simultaneously

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  • Xiao-qiu Shi
  • Wei Long
  • Yan-yan Li
  • Ding-shan Deng
  • Yong-lai Wei
  • Hua-guo Liu

Abstract

Supply networks (SN) must maintain operations and connectedness under disruptions to remain competitive; this is referred to as SN resilience. Building a resilient SN is an underlying challenge in supply chain management. In this paper, SN resilience is examined from the complex network topology perspective to understand how supply chain managers construct resilient networks. The proposed growth model considers enterprises leaving the network, which previous studies have ignored. Considering the heterogeneous roles of enterprises, new metrics based on a new proposed sub-network concept are presented to evaluate resilience. Using a computer simulation, the resilience of the SN generated by the model proposed in this paper is compared with that of other models, and the results indicate that (i) the proposed model can be tuned to generate a desired resilient network; (ii) the proposed metrics capture the resilience requirements of the SN very well; (iii) the more uniform the distribution of the enterprises, the more resilient the corresponding SN; and (iv) the higher the values of α and β, the lower the SN resilience, and β affects the resilience more than α does.

Suggested Citation

  • Xiao-qiu Shi & Wei Long & Yan-yan Li & Ding-shan Deng & Yong-lai Wei & Hua-guo Liu, 2020. "Research on supply network resilience considering random and targeted disruptions simultaneously," International Journal of Production Research, Taylor & Francis Journals, vol. 58(21), pages 6670-6688, November.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:21:p:6670-6688
    DOI: 10.1080/00207543.2019.1685697
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    Cited by:

    1. Olfati, Marjan & Paydar, Mohammad Mahdi, 2023. "Towards a responsive-sustainable-resilient tea supply chain network design under uncertainty using big data," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Jin, Pengfei & Wang, Saige & Meng, Zheng & Chen, Bin, 2023. "China's lithium supply chains: Network evolution and resilience assessment," Resources Policy, Elsevier, vol. 87(PB).
    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).
    4. Shi, Xiaoqiu & Long, Wei & Li, Yanyan & Deng, Dingshan, 2022. "Robustness of interdependent supply chain networks against both functional and structural cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    5. Liu, Hui & Su, Bingbing & Guo, Min & Wang, Jingbei, 2024. "Exploring R&D network resilience under risk propagation: An organizational learning perspective," International Journal of Production Economics, Elsevier, vol. 273(C).
    6. Xiongping Yue & Dong Mu & Chao Wang & Huanyu Ren & Jianbang Du & Pezhman Ghadimi, 2024. "Disruption risks to Chinese overseas flat panel display supply networks under China’s zero-COVID policy," Operations Management Research, Springer, vol. 17(2), pages 406-437, June.

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