Abstract

Supply chain waste manifests through multiple mechanisms including excess inventory accumulation, stockouts leading to lost sales and expedited fulfillment costs, product obsolescence in dynamic markets, and systematic forecast errors that propagate throughout distribution networks. These inefficiencies impose substantial operational and financial burdens while undermining sustainability objectives through resource misallocation and increased carbon emissions. This article examines how artificial intelligence-enabled demand forecasting and inventory optimization can systematically reduce waste across multi-tier supply chains. Advanced AI techniques including probabilistic forecasting methods, deep learning architectures for pattern recognition in complex demand signals, and reinforcement learning for dynamic policy adaptation enable more accurate demand predictions and responsive inventory positioning. Multi-echelon inventory planning frameworks optimize stock allocation across distribution layers, while safety stock optimization balances service-level requirements against holding costs under uncertainty. Dynamic replenishment strategies leverage real-time data streams to adjust ordering decisions continuously. Empirical evidence demonstrates that these approaches yield substantial cost reductions through decreased holding and shortage costs, improved service-level attainment through better availability, and enhanced sustainability outcomes via reduced waste generation and resource consumption. Future directions include the development of explainable AI systems that provide transparent decision rationale, real-time decision intelligence platforms that integrate sensing and actuation capabilities, and explicit consideration of ethical dimensions in automated planning systems including fairness, accountability, and human oversight requirements.