Supply Chain Resilience: A Common Weights Efficiency Analysis with Non-discretionary and Non-controllable Inputs

Reza Kiani Mavi (), Neda Kiani Mavi (), Seyed Ashkan Hosseini Shekarabi (), Matthew Pepper’s () and Sean Arisian ()
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Reza Kiani Mavi: Edith Cowan University
Neda Kiani Mavi: Edith Cowan University
Seyed Ashkan Hosseini Shekarabi: Edith Cowan University
Matthew Pepper’s: University of Wollongong
Sean Arisian: La Trobe Business School, La Trobe University

Global Journal of Flexible Systems Management, 2023, vol. 24, issue 1, No 6, 77-99

Abstract: Abstract Supply chain resilience (SCRes) as the supply chain network's (SCN) capacity is essential to recover from disruptions. The economic, environmental, and geopolitical regional characteristics of the Pacific region present many challenges and opportunities for building supply chain resilience. This study aims to measure the resilience of supply chains (SCs) considering the characteristics of the network under which they operate. In this study, we proposed a new common set of weights (CSW) model in data envelopment analysis to evaluate the resilience of SCNs. Many external variables beyond decision-makers’ direct control impact SC operations and their resilience. Therefore, the proposed CSW model formulates the non-discretionary and non-controllable inputs in measuring the resilience of SCNs and provides a complete ranking with a higher discrimination power. To improve SCRes, SC managers are recommended to enhance the clustering coefficient and node degree of their SCN by establishing more connections with other SCNs in order to pinpoint the essential capabilities that companies should prioritise in order to develop a stronger and more adaptable SC in the post-COVID-19 pandemic.

Keywords: Common weights; Data envelopment analysis; Disruption; Efficiency; Flexibility; Pacific region; Supply chain resilience; Vulnerability (search for similar items in EconPapers)
JEL-codes: C61 C67 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40171-024-00380-5

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