AI-Enabled Demand Forecasting, Technological Capability, and Supply Chain Performance: Empirical Evidence from the Global Logistics Sector

This study advances understanding of artificial intelligence (AI) integration within supply chain management, with a particular emphasis on AI-enabled demand forecasting. The research examines 1) the extent of adoption of AI-driven forecasting practices, 2) the role of technological and organizational readiness, captured through data infrastructure, workforce skills, and management support, as antecedents, and 3) the mediating effect of AI adoption on the relationship between readiness and supply chain performance. Grounded in the resource-based view and technology adoption theory, a conceptual model was developed and empirically validated using data from global logistics firms, with structural equation modeling applied as the primary analytical technique. The findings confirm that readiness factors significantly foster AI adoption, which in turn exerts both a direct effect on supply chain performance and a mediating effect linking readiness to performance. By focusing on the global logistics sector and empirically validating this mediating mechanism, the study provides novel insights into how firms can translate technological readiness into superior operational outcomes, offering theoretical contributions to AI assimilation literature and practical guidance for managers.