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A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study

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Abstract

Today’s complex supply chains are increasingly susceptible to the turbulent and fast-changing business environment and their economic implications. Resilience as an effective strategic planning during disturbances is a way to mitigate supply chain vulnerabilities. After reviewing the literature on the topic of a resilient supply chain, this paper extracts a complete series of 16 resilience enablers. These identified enablers form the foundation of a questionnaire distributed among over 150 experts and staffs of a real case associated with an Iranian automotive supply chain. The reliability and validity of the questionnaire are evaluated by statistical tests and Cronbach’s alpha. Then, a hybrid of the Z-number data envelopment analysis and neural network is employed for the efficiency score calculation, separately. Finally, the associated results are combined and the final efficiency scores are obtained. The case study findings indicate that by improving the resilience enablers, especially ones with the greatest influence on the supply chain performance, firms can be less vulnerable in times of supply chain disruptions. The framework proposed in this study may find a broad practical application in all types of supply chains.

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Correspondence to Reza Tavakkoli-Moghaddam.

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Yazdanparast, R., Tavakkoli-Moghaddam, R., Heidari, R. et al. A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study. Cent Eur J Oper Res 29, 611–631 (2021). https://doi.org/10.1007/s10100-018-0596-x

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