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The Multi-Level Hierarchical Structure of the Enablers for Supply Chain Resilience Using Cloud Model-DEMATEL–ISM Method

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  • Jih-Kuang Chen

    (Economics and Management College, Zhaoqing University, Zhaoqing 526060, China)

  • Tien-Yu Huang

    (Economics and Management College, Zhaoqing University, Zhaoqing 526060, China)

Abstract

Companies must shift from traditional supply chain management thinking to addressing or preventing increases in vulnerability, uncertainty, and unforeseen supply chain disruptions facing complex global supply chains. Systems with a large number of elements may be susceptible to nonlinear interactions, perturbation of which may lead to serious impacts. Thus, there is an increasing need to determine the importance of individual elements and how these elements interact. Published studies of supply chain resilience (SCRes) do not clearly determine the hierarchical structure of factors, and the understanding of interactions between factors remains fragmented. In this study, we proposed a cloud model-DEMATEL−ISM method to overcome the disadvantages of traditional DEMATEL−ISM integration methods. The MICMAC method (cross-impact matrix multiplication applied to classification) was also used to classify the enablers of SCRes based on driving force and dependence force. We tested these approaches by studying the new energy vehicle industry in China. The results suggest that companies trying to strengthen SCRes should focus on enablers at the base layer with a high driving force, particularly the enablers of social capital, restructuring, risk management culture, information technology application, trust and collaboration, information sharing, and learning capability..

Suggested Citation

  • Jih-Kuang Chen & Tien-Yu Huang, 2022. "The Multi-Level Hierarchical Structure of the Enablers for Supply Chain Resilience Using Cloud Model-DEMATEL–ISM Method," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12116-:d:924672
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    Cited by:

    1. Mengmeng Wang & Xiaoming Pan, 2022. "Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    2. Weijia Li & Ziyang Liu, 2023. "Social, Environmental, and Governance Factors on Supply-Chain Performance with Mediating Technology Adoption," Sustainability, MDPI, vol. 15(14), pages 1-15, July.

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