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<p>Global supply chains face rising levels of disruption, which demand stronger flexibility and resilience. Organizations must understand how strategic, operational, and digital capabilities work together to support these outcomes. This study examines how agility, redundancy, supply chain operations, and BDA-AI capabilities contribute to flexibility as a mechanism and resilience as an outcome. It also clarifies the distinct roles of these capabilities in shaping adaptive responses. This study develops a hybrid Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) framework to examine how strategic (agility, redundancy), operational, and AI-based analytics capabilities interact to build adaptive supply chain resilience. A survey of manufacturing professionals provides the empirical data. Results show that redundancy and strong operations are the strongest predictors of BDA-AI capability. Agility also supports resilience, but its influence is smaller than redundancy and operations. BDA-AI strengthens resilience by improving sensing, interpretation, and response ability. The hybrid model reveals important nonlinear patterns, highlighting the combined contribution of strategy, operations, and digital analytics. These findings show that resilience does not arise from one capability alone but from the interaction of routines, and digital intelligence. The study offers actionable guidance for managers by emphasizing the need to strengthen operations and redundancy before scaling AI-driven analytics. This staged approach can enhance flexibility and support resilient performance during disruption.</p>

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From Resilience to Flexibility: A Hybrid SEM-ANN Model of AI-Enabled Adaptive Supply Chains

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Talat Mahmud Chowdhury,
  • Kazi Md. Tanvir Anzum

摘要

Global supply chains face rising levels of disruption, which demand stronger flexibility and resilience. Organizations must understand how strategic, operational, and digital capabilities work together to support these outcomes. This study examines how agility, redundancy, supply chain operations, and BDA-AI capabilities contribute to flexibility as a mechanism and resilience as an outcome. It also clarifies the distinct roles of these capabilities in shaping adaptive responses. This study develops a hybrid Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) framework to examine how strategic (agility, redundancy), operational, and AI-based analytics capabilities interact to build adaptive supply chain resilience. A survey of manufacturing professionals provides the empirical data. Results show that redundancy and strong operations are the strongest predictors of BDA-AI capability. Agility also supports resilience, but its influence is smaller than redundancy and operations. BDA-AI strengthens resilience by improving sensing, interpretation, and response ability. The hybrid model reveals important nonlinear patterns, highlighting the combined contribution of strategy, operations, and digital analytics. These findings show that resilience does not arise from one capability alone but from the interaction of routines, and digital intelligence. The study offers actionable guidance for managers by emphasizing the need to strengthen operations and redundancy before scaling AI-driven analytics. This staged approach can enhance flexibility and support resilient performance during disruption.