AI-Driven Optimization of Warehouse Layout and Material Handling: A Quantitative Study on Efficiency and Space Utilization

Abstract

This quantitative study explores the transformative role of artificial intelligence (AI) in optimizing warehouse layout and material handling processes, with a specific focus on improving efficiency and space utilization in high-demand, high-complexity logistics environments. Drawing on a systematic review of 142 peer-reviewed academic articles published between 2010 and 2025, the research examines the performance impact of AI-driven systems across various warehouse functions, including slotting optimization, real-time task allocation, autonomous routing, and inventory traceability. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological transparency, rigor, and reproducibility. Through in-depth synthesis and comparative analysis, the findings reveal that AI technologies—particularly reinforcement learning, supervised machine learning, and hybrid AI architectures—consistently yield significant operational improvements, including 15%–45% reductions in cycle times, 20%–35% gains in volumetric space utilization, and notable increases in order accuracy above 98%. Moreover, the study identifies key performance differentials across industry contexts and AI techniques, emphasizing the importance of customized, domain-specific implementations. While the results strongly support AI’s capacity to elevate warehouse productivity, the study also highlights critical research gaps, including a lack of real-time operational data, inconsistent benchmarking practices, and limited cross-industry generalizability. Recommendations are provided for both practitioners and researchers, advocating for the development of integrated AI-WMS systems, standardized evaluation frameworks, and long-term studies that address scalability, workforce integration, and sustainability. This research contributes to the growing body of logistics and operations literature by offering a comprehensive, data-driven assessment of AI’s effectiveness in transforming modern warehouse systems and lays a foundation for future empirical and applied innovation in intelligent supply chain optimization.