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Purpose

Supply chain management is an operational solution for the survival of companies in today’s competitive world. Supply chain management is challenging, and the dynamic behavior of the supply chain increases its complexity. Although a structured supply chain has high operational efficiency, risks cannot be ignored. Antifragility is characteristic of systems that provide the possibility of improvement due to exposure to stressful factors, shocks, fluctuations, noise and uncertainty in general. This study, through focusing on the textile industry, tries to understand stressful factors and describing their fragility, to provide criteria for measuring the fragility of this industry.

Design/methodology/approach

In this study, with the help of the fuzzy cognitive mapping (FCM) method, the causal relationships of the fragility of textile companies’ supply chains were investigated under uncertain conditions. Also, by creating different scenarios, the relative changes of components over other components were determined.

Findings

The results show that the three variables with the highest centrality index include the lack of investment in the industry (clothing) (A6), importing clothes from cheap producing countries (A7) and saturation of the market with fake brands due to the structural weakness of the distribution and smuggling network (A8). Also, the three variables with the lowest centrality index include lack of skilled and experienced manpower in the knitting and sewing chain (A14), lack of specialized and experienced manpower in the chain of dyeing, printing and finishing (A20) and lack of specialized and experienced manpower in the spinning chain (A3). Finally, seven scenarios were developed using the results of the fuzzy cognitive map to investigate the relative changes of supply chain fragility factors and its effect on other factors.

Originality/value

This research examines that the causal relationships of antifragility of the supply chain of textile companies under conditions of uncertainty using a FCM will be used to examine the relationships of model components.

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