Supply Chain Resilience in the Textile and Apparel Industry: Predictive Analytics, Digital Technologies, and Lessons from Global Disruptions

Abstract

The textile and apparel industry operates within one of the world's most complex and globally distributed manufacturing networks, making it particularly vulnerable to disruptions arising from geopolitical instability, transportation bottlenecks, climate-related events, and demand uncertainty. This narrative review critically examines recent advances in supply chain resilience engineering by synthesizing research on predictive analytics, digital twin technologies, and Industry 4.0 systems within textile and apparel supply chains. A structured narrative review methodology was employed to identify, evaluate, and comparatively analyze peer-reviewed studies addressing engineering approaches to resilient manufacturing and logistics systems. The review demonstrates that supply chain resilience has evolved from conventional strategies based on inventory redundancy and supplier diversification toward adaptive engineering systems integrating predictive intelligence, real-time operational visibility, and dynamic decision support. Comparative analysis indicates that predictive analytics and digital twins provide complementary capabilities, enabling proactive disruption prediction, virtual system simulation, and optimization of production, inventory, and logistics decisions. However, widespread implementation remains constrained by fragmented digital infrastructures, limited data interoperability, high implementation costs, and the operational characteristics of low-margin manufacturing environments. Based on the synthesized evidence, this review proposes an integrated engineering framework that combines operational data acquisition, predictive analytics, digital twin simulation, engineering optimization, and adaptive manufacturing execution to strengthen supply chain resilience. The review further identifies future research priorities focusing on scalable digital twin architectures, interoperable engineering platforms, and optimization-driven decision-support systems. These findings provide both a comprehensive synthesis of current engineering knowledge and a practical roadmap for developing adaptive, data-driven, and resilient textile and apparel supply chains.