AI Driven Optimization in Specific SCM Domains: Warehousing, Logistics, Transport

Abstract

The continually surging complexity of global supply chains has placed greater demands on smart optimization of warehousing, logistics, and transport, which are the operating core of supply chain management (SCM) operations. Artificial intelligence (AI) has transformative potential by leveraging predictive analytics, machine learning, deep learning, and reinforcement learning to deliver efficiency, resilience, and sustainability. This paper reviews subject-specific applications of AI in SCM, providing a structured analysis of optimization in the areas of warehousing, logistics, and transport. At warehouses, AI can conduct automated slotting, robotized picking, and layouts with a boost of digital twins, which reduce costs and step up throughput. In logistics, intelligent algorithms make dynamic routing, last-mile optimization, and predictive load balancing feasible, which enhances customer service and tames disruptions. At transport, AI can facilitate fleet management, predictive maintenance, reduction of emissions, and integration of autonomous vehicles and unmanned aerial vehicles (drones). With a comprehensive literature analysis and conceptual integration, the paper, through its findings, sheds both emergent benefits and lingering shortcomings such as data fragmentation, interoperability, workforce adaptation, and regulatory limitations. This paper makes a value addition to the literature by comparing the role of AI within such domains and describing gaps that thwart complete-scale integration. The paper's findings suggest that optimization by AI is not merely a technological enhancement, but a strategic imperative for building resilient, adaptive, and sustainable SCM ecosystems. Future work should focus on explainable AI, cross-domain integration, and aligning AI adoption with global decarbonization and circular economy goals.