Traditional warehouse management systems face unprecedented challenges in the Industry 4.0 era, including escalating e-commerce demands, acute labor shortages, and critical requirements for real-time inventory visibility. Existing solutions fail to deliver the flexibility, scalability, and operational efficiency essential for contemporary supply chain operations. A novel integration framework combining Autonomous Mobile Robots (AMR) with Cyber-Physical Systems (CPS) is presented to enable intelligent, adaptive inventory management in smart warehouse environments. A multi-layered CPS architecture incorporating AMR fleet coordination, real-time data analytics, and digital twin synchronization is proposed. The framework employs distributed task allocation algorithms, dynamic path planning strategies, and predictive inventory optimization models. Implementation leverages edge computing for real-time decision-making and cloud infrastructure for comprehensive data analysis and storage. Experimental validation in industrial environments demonstrates significant performance improvements: 42% enhancement in order fulfillment speed, 35% reduction in inventory holding costs, and 89% accuracy in real-time stock tracking. The system maintained 99.2% uptime reliability while successfully managing 3× peak demand variations. The research advances smart logistics by establishing a scalable, generalizable CPS-AMR framework applicable across diverse warehouse environments. The findings provide actionable guidelines for Industry 4.0 transformation initiatives and establish theoretical foundations for next-generation autonomous warehouse systems.
Zhang et al. (Sat,) studied this question.