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<p>Supply chain management (SCM) is being transformed by the rapid proliferation of digital technologies that open up pathways to greater resilience and agility. This study examines the impact of new technologies, such as cloud computing, blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics, on the performance of SCM in India. Using a mixed-methods research design, this study fills an important literature gap, as most previous studies have examined these technologies separately. The study used measurement, structural modeling, and importance and performance analysis (IPMA) to identify the primary elements that can enhance SCM outcomes. The results suggest that digital technology adoption (DTA), data integration (DI) and predictive analytics (PA) are critical factors for SCM performance in improving supply chain flexibility and reliability. PLS-Predict was applied to evaluate the predictive performance out-of-sample and the model proved to be robust. This work contributes to the growing knowledge base since it provides empirical evidence on how digital tools optimize SCM best and thus delivers companies' strategic insights on balancing proximity to demand centers and supply chain resilience.</p>

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An empirical assessment of technological advancements on supply chain management performance: a mixed-methods sem approach using smartpls

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Ganesh Kumar R,
  • Dinesh Kumar S,
  • C. Anirvinna,
  • Rapaka David Goodwin

摘要

Supply chain management (SCM) is being transformed by the rapid proliferation of digital technologies that open up pathways to greater resilience and agility. This study examines the impact of new technologies, such as cloud computing, blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics, on the performance of SCM in India. Using a mixed-methods research design, this study fills an important literature gap, as most previous studies have examined these technologies separately. The study used measurement, structural modeling, and importance and performance analysis (IPMA) to identify the primary elements that can enhance SCM outcomes. The results suggest that digital technology adoption (DTA), data integration (DI) and predictive analytics (PA) are critical factors for SCM performance in improving supply chain flexibility and reliability. PLS-Predict was applied to evaluate the predictive performance out-of-sample and the model proved to be robust. This work contributes to the growing knowledge base since it provides empirical evidence on how digital tools optimize SCM best and thus delivers companies' strategic insights on balancing proximity to demand centers and supply chain resilience.