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Apurba Sarker

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Review Open access Jul 2026

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

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.

Apurba Sarker · British Journal of Multidisc... · 0 citations

Open access Aug 2026

LCO-sensitive graph neural network framework for high-accuracy energy mapping in NiCoCr medium-entropy alloys

Machine learning (ML) techniques have become pivotal in material design, yet accurately capturing the subtle energy shifts associated with local chemical order (LCO) in multi-principal element alloys remains a significant challenge. This study establishes a graph convolutional neural network (GCNN) framework specifically engineered to map the potential energy landscape of NiCoCr medium-entropy alloys with high sensitivity to LCO transitions. Utilizing hybrid Monte-Carlo molecular dynamics simulations as a systematic evaluation benchmark, we evaluate the GCNN’s capacity to learn the non-linear relationship between atomic arrangements and structural stability across varying thermal regimes. The atomic configurations are transformed into graph representations, where nodes incorporate atom types and absolute velocities, and edges are established with the 12 nearest neighbours to encapsulate the local chemical environment. The model’s fitting and predictive fidelity was assessed through three distinct case studies: individual thermal datasets, combined temperature ranges, and unseen configurations. The GCNN demonstrates exceptional performance, achieving coefficient of determination R2 values up to 0.98 and a mean absolute error as low as 1.04 meV atom−1 on entirely withheld thermal trajectories (550 K), effectively tracking the energy variations correlated with the system’s LCO evolution. This research offers a comprehensive graph-based modelling approach for understanding the configuration-to-energy mapping relationships in complex multi-principal element alloys, providing a baseline structural architecture that can be extended toward ML interatomic potential workflows.

Mashaekh Tausif Ehsan, Saifuddin Zafar, Apurba Sarker et al. · Modelling and Simulation in... · 0 citations