AI in Supply Chain Transportation: Optimizing Costs Through Predictive Analytics

Abstract

Transportation inefficiencies within supply chains impose substantial hidden costs that ultimately inflate consumer prices at retail. This article examines how artificial intelligence applications address these inefficiencies through predictive analytics and optimization algorithms across multiple operational dimensions. The article explores AI-driven solutions for demand forecasting, shortage prediction, arrival time estimation, and warehouse resource management, analyzing their collective impact on transportation cost structures. Using a combination of case studies and real data, the article shows how predictive abilities help companies allocate resources more effectively, which lowers costs related to extra labor, crowded warehouses, delays with trailers, and poor choices in carrier selection. The article reveals that organizations implementing AI-powered transportation management systems achieve meaningful operational improvements and cost reductions that flow through to final retail pricing equations. While implementation challenges, including data quality requirements, system integration complexity, and organizational change management, present obstacles, successful adopters realize sustained competitive advantages. The article establishes that AI technologies have evolved from experimental innovations to essential capabilities for supply chain competitiveness. This article contributes to the theoretical understanding of cost pass-through mechanisms in retail pricing while providing practical insights for supply chain managers, retailers, and policymakers navigating digital transformation. The article identifies future research directions, including holistic supply chain integration, advanced algorithmic techniques, sustainability considerations, and scalability across diverse organizational contexts.