Optimizing In-Store Logistics: How AI Enhances Inventory Management and Space Utilization

Abstract

This integrative literature review looks at the revolutionary impact of artificial intelligence (AI) in optimizing in-store logistics to assist retail managers and technology decision-makers in using AI to improve inventory management, spatial organization, and customer experience. Based on six core concepts—AI-driven demand forecasting, automated inventory replenishment, space utilization optimization, adaptive store layout design, operational efficiency, and customer satisfaction—the study's conceptual framework emphasizes AI's strategic value and the factors driving its adoption in retail logistics. The review uses rigorous criteria and systematic analysis of peer-reviewed articles, industry reports, and case studies to identify significant topics such as AI-enhanced demand forecasting, automated restocking, responsive shop layouts, data protection, and the changing responsibilities of retail staff. The paper advocates for balanced AI integration, integrating technology breakthroughs with human control and appropriate data management. Future research proposals include investigating AI's long-term implications, doing comparative assessments across retail forms, and developing frameworks for ethical data usage. These will all provide foundational insights for constructing sustainable, sophisticated retail environments that align with global development goals.