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A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators

Author

Listed:
  • Iman Kazemian

    (University of Tehran)

  • S. Ali Torabi

    (University of Tehran)

  • Christopher W. Zobel

    (Virginia Tech)

  • Yuhong Li

    (Old Dominion University)

  • Milad Baghersad

    (Florida Atlantic University)

Abstract

This study proposes a supply chain resilience assessment framework at the network (i.e. structural) level based on quantifying supply chain networks’ structural factors and their relationships to different resilience strategies, by using a hybrid DEMATEL–ANP approach. DEMATEL is used to quantify interdependencies between the structural resilience factors, and between the resilience strategies. ANP is then used to quantify the outer-dependencies among these elements and to construct the limit super-matrix from which the global weights of all the decision network’s elements are estimated. To create the structural resilience factors, different network factors are selected and adopted from the social network analysis and supply chain resilience literatures. A case study is then performed to assess the performance of the proposed approach and to derive important observations to support future decision making. According to the results, the proposed approach can suitably measure the resilience performance of a supply chain network and help decision makers plan for more effective resilience improvement actions.

Suggested Citation

  • Iman Kazemian & S. Ali Torabi & Christopher W. Zobel & Yuhong Li & Milad Baghersad, 2022. "A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators," Operational Research, Springer, vol. 22(3), pages 1853-1883, July.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:3:d:10.1007_s12351-021-00644-3
    DOI: 10.1007/s12351-021-00644-3
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    Cited by:

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    2. Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.

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