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

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.