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

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

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 languageEnglish
Pages (from-to)1256-1276
Number of pages21
JournalIISE Transactions
Volume55
Issue number12
Early online date6 Feb 2023
DOIs
Publication statusPublished - 28 Mar 2023

Bibliographical note

Funding Information:
Comments and guidance provided by Department Editor Professor Jennifer Ryan, the Associate Editor and two anonymous reviewers have significantly improved this article.

Publisher Copyright:
© Copyright © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

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