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

Reza Yazdanparast, Reza Tavakkoli-Moghaddam (), Razieh Heidari and Leyla Aliabadi
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Reza Yazdanparast: University of Tehran
Reza Tavakkoli-Moghaddam: University of Tehran
Razieh Heidari: University of Tehran
Leyla Aliabadi: University of Tehran

Central European Journal of Operations Research, 2021, vol. 29, issue 2, No 13, 631 pages

Abstract: 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.

Keywords: Supply chain resilience; Vulnerability; Z-numbers data envelopment analysis; Neural network; Automotive supply chain (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10100-018-0596-x

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