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

Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Giulio Marcucci and Giovanni Mazzuto

International Journal of Production Research, 2020, vol. 58, issue 20, 6370-6398

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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2019.1680893 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:58:y:2020:i:20:p:6370-6398

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2019.1680893

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().