Developing Capabilities for Supply Chain Resilience in a Post-COVID World:  A Machine Learning based Thematic Analysis

Dun Li; Bangdong Zhi; Tobias Schoenherr; Xiaojun Wang

doi:10.1080/24725854.2023.2176951

Developing Capabilities for Supply Chain Resilience in a Post-COVID World: A Machine Learning based Thematic Analysis

Dun Li
, Bangdong Zhi^*
, Tobias Schoenherr
, Xiaojun Wang

^*Corresponding author for this work

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Abstract

This study examines the past, present, and future of supply chain resilience (SCR) research in the context of COVID-19. Specifically, a total of 1,717 papers in the SCR field are classified into eleven thematic clusters, which are subsequently verified by a supervised machine learning approach. Each cluster is then analyzed within the context of COVID-19, leading to the identification of three associated capabilities (i.e., interconnectedness, transformability, and sharing) that firms should focus on to build a more resilient supply chain in the post-COVID world. The derived insights offer invaluable guidance not only for practicing managers, but also for scholars as they design their future research projects related to SCR for greatest impact.

Original language	English
Pages (from-to)	1256–1276
Number of pages	21
Journal	IISE Transactions
Volume	55
Issue number	12
Early online date	6 Feb 2023
DOIs	https://doi.org/10.1080/24725854.2023.2176951
Publication status	Published - 22 Nov 2023

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Developing Capabilities for Supply Chain Resilience in a Post-COVID World: A Machine Learning based Thematic Analysis

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