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

Tseng, M.-L., Bui, T.-D., Lim, M. K. , Fujii, M. and Mishra, U. (2022) Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity. International Journal of Production Economics, 245, 108401. (doi: 10.1016/j.ijpe.2021.108401)

Abstract

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.

• Item Type: Articles
• Additional Information: This study is funded by Ministry of Science and Technology, Taiwan. Grant number: 110-2221-E-468 -010 -.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Tseng, M.-L., Bui, T.-D., Lim, M. K., Fujii, M., and Mishra, U.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: International Journal of Production Economics
• Publisher: Elsevier
• ISSN: 0925-5273
• ISSN (Online): 1873-7579
• Published Online: 27 December 2021
• Copyright Holders: Copyright © 2021 Elsevier B.V.
• First Published: First published in International Journal of Production Economics 245: 108401
• Publisher Policy: Reproduced in accordance with the publisher copyright policy