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Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study

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  • Maurizio Bevilacqua
  • Filippo Emanuele Ciarapica
  • Giulio Marcucci
  • Giovanni Mazzuto

Abstract

The domino effect that occurs among the concepts that affect Supply Chain Resilience has only been marginally analysed, and no conceptual models have been proposed in the literature. In this work, a conceptual model for analysing this domino effect is developed. The method aims to identify which supply chain concepts can support the containment of disruptions and how these concepts affect one another. The proposed methodology is based on Fuzzy Cognitive Maps. The Cognitive Maps tool enables us to connect multidimensional and multidisciplinary concepts (e.g. sources of risk, disruption factors, supply chain management practices and other aspects). Moreover, this tool allows company managers to develop a detailed understanding of a system's behaviour and to explicitly consider the mind models of different players in the supply chain. A case study of the fashion industry supply chain is used to illustrate the application of the proposed method in an operating context. The proposed method enables a company to evaluate the hidden chain reaction of causes behind the most important factors that, from a single trigger event, are able to harm the entire Supply Chain. Through analysis of the causal relationships that this methodology highlights, decision makers can examine the domino effect among the concepts that influence Supply Chain Resilience in a step-by-step manner.

Suggested Citation

  • Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Giulio Marcucci & Giovanni Mazzuto, 2020. "Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study," International Journal of Production Research, Taylor & Francis Journals, vol. 58(20), pages 6370-6398, October.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:20:p:6370-6398
    DOI: 10.1080/00207543.2019.1680893
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    Cited by:

    1. Bag, Surajit & Sabbir Rahman, Muhammad & Rogers, Helen & Srivastava, Gautam & Harm Christiaan Pretorius, Jan, 2023. "Climate change adaptation and disaster risk reduction in the garment industry supply chain network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    2. Fabio Di Carlo & Giovanni Mazzuto & Maurizio Bevilacqua & Filippo Emanuele Ciarapica, 2021. "Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    3. Chih-Hung Hsu & Ming-Ge Li & Ting-Yi Zhang & An-Yuan Chang & Shu-Zhen Shangguan & Wan-Ling Liu, 2022. "Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework," Mathematics, MDPI, vol. 10(8), pages 1-35, April.
    4. Tomás F. Espino-Rodríguez, 2023. "Research on Outsourcing by Hotel Firms: Current State and Future Directions," Tourism and Hospitality, MDPI, vol. 4(1), pages 1-15, January.
    5. Beata Milewska, 2022. "The Impact of the COVID-19 Pandemic on Supply Chains in the Example of Polish Clothing Companies in the Context of Sustainable Development," Sustainability, MDPI, vol. 14(3), pages 1-19, February.
    6. Jia, Nanfei & An, Haizhong & Gao, Xiangyun & Liu, Donghui & Chang, Hao, 2023. "The main transmission paths of price fluctuations for tungsten products along the industry chain," Resources Policy, Elsevier, vol. 80(C).

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