Intelligent Warehouse Management System Using Robotics and IoT Technologies

Abstract

The increasing complexity of supply chains and the demand for real-time inventory visibility have accelerated the adoption of intelligent warehouse management systems (IWMS) integrating robotics and Internet of Things (IoT) technologies. Intelligent warehouses leverage automated guided vehicles, robotic arms, smart sensors, and IoT devices to optimize storage, picking, sorting, and inventory monitoring processes. This study develops and empirically validates a conceptual framework examining the impact of robotic automation, IoT sensor integration, data analytics capability, and system interoperability on operational efficiency, inventory accuracy, and decision-making effectiveness in warehouse environments. Drawing on socio-technical systems theory and cyber-physical systems frameworks, the research conceptualizes operational efficiency, inventory accuracy, and decision-making effectiveness as dependent constructs influenced by technological and integration factors. A quantitative research design using Partial Least Squares Structural Equation Modeling was adopted. Data were collected from 398 warehouse managers, logistics engineers, and IT specialists across retail, manufacturing, and e-commerce sectors. Measurement model assessment confirmed reliability and convergent validity with composite reliability values above 0.90 and average variance extracted above 0.61. Structural model results indicate that robotic automation beta 0.53 p less than 0.001, IoT sensor integration beta 0.48 p less than 0.001, and system interoperability beta 0.41 p less than 0.001 positively influence operational efficiency. Inventory accuracy mediates the relationship between IoT integration and decision-making effectiveness beta 0.44 p less than 0.001. The model explains 63 percent of variance in operational efficiency and 59 percent in decision-making effectiveness. Findings demonstrate that the integration of robotics and IoT technologies significantly enhances warehouse performance and supports data-driven decision-making. The study provides a validated interdisciplinary framework to guide practitioners and policymakers in designing intelligent, efficient, and scalable warehouse management systems