This research aims to examine the integration of advanced technologies and organizational capabilities to achieve sustainable performance in modern supply chains. By synthesizing insights from people analytics, AI-enabled supply chain management, supply chain absorptive capacity, supply chain ambidexterity and optimization strategies, this study proposes a holistic framework for optimizing sustainability outcomes.
This study employs a deductive approach, utilizing quantitative methods and a survey technique to collect data from its intended participants. The research adopted a systematic random sampling method, surveying 542 individuals employed in various manufacturing sectors across New York, Texas and California.
The study researches multidimensional strategies for enhancing sustainable performance. Results indicated that People Analytics and AI-enabled supply chains (SCs) influence the organizational absorptive capacity (OAC), which in turn fosters organizational ambidexterity (OA). These capabilities enable process optimization and resource optimization, ultimately driving sustainable manufacturing. The results reveals several strong structural relationships, including the influence of OAC on OA and its subsequent impact on both optimization and sustainability dimensions, providing clarity on how dynamic capabilities interrelate within a supply chain context.
This study presents a novel framework that proposes and empirically validates a comprehensive, higher-order integration of people analytics, AI-enabled supply chains, organizational absorptive capacity and ambidexterity to drive sustainable manufacturing performance. It uniquely combines human-centric and technological capabilities to explain how firms can optimize both resources and processes. By examining their synergistic effects on environmental, economic, and social performance dimensions, the research fills a notable gap in the sustainability literature of SC. The study advances theory by positioning absorptive capacity as a dynamic capability mediating the impact of analytics and AI on organizational ambidexterity, a critical yet underexplored link. Practically, the findings offer actionable insights for manufacturing managers and policymakers on how to operationalize people analytics and AI not in isolation, but as complementary tools for building knowledge-driven, agile and sustainable manufacturing SCs.
