Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity

Ming Lang Tseng, Tat Dat Bui, Ming K. Lim, Minoru Fujii, Umakanta Mishra

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)
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This study contributes to developing the existing knowledge regarding data-driven sustainable supply chain management (SSCM) indicators under industrial disruption and ambidexterity. SSCM is a type of information flow management that facilitates cooperation and collaboration among supply chain players and stakeholders while considering economic, social, and environmental perspectives. Previous studies have failed to (1) generate these indicators from databases and confirm the validity of the effective indicators; (2) build a hierarchical structure with interrelationships under industrial disruption and ambidexterity; and (3) identify the indicators necessary for effective textile performance. The proposed hybrid method generates indicators from a database and based on the existing literature. This study proposes using the fuzzy Delphi method to validate these indicators in the textile industry and applies the best and worst methods to examine the most effective and ineffective indicators. Valid aspects and criteria are used to construct a hierarchical structure under conditions of industrial disruption and ambidexterity. The results show that the most important aspects are financial vulnerability, supply chain uncertainty, risk assessment, and resilience; these aspects are drivers that are guaranteed to ensure the effectiveness of SSCM under industrial disruption and ambidexterity. Financial crisis response, business continuity, supply chain integration, bullwhip effect, facility location, and supplier selection are highlighted as vital practical strategies.

Original languageEnglish
Article number108401
Number of pages20
JournalInternational Journal of Production Economics
Early online date27 Dec 2021
Publication statusPublished - 1 Mar 2022

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This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.


This study is funded by Ministry of Science and Technology , Taiwan. Grant number: 110-2221-E-468 -010 - .

Publisher Copyright:
© 2021 Elsevier B.V.


  • Best and worst method
  • Disruption and ambidexterity
  • Fuzzy delphi method
  • Sustainable supply chain management

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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