Digital Twin Technology and IoT-Enabled AI Using Real-Time Analytics for Smart Warehouse Management and Predictive Inventory Optimization

Abstract

Digital Twin Technology (DTT) and AI-powered solutions of IoT have become game-changer solutions in the smart warehouse management landscape, enabling real-time analytics for predictive inventory optimization. These technologies allow the seamless integration of IoTsensors, Waehouse Management Systems (WMS), and machine learning to track and get optimized warehouse operations. DTT and IoT work together to allow for better decision making and resource allocation through the continual monitoring of inventory levels, product movements and environmental factors, thereby reducing operational costs and improving efficiency. This
study aims to examine the role of DTT and IoT-integrated AI in improving warehouse management systems by assessing its influence on predictive inventory forecasting and real-time decision-making. Summary robots train on up to Oct'2022 data with robot robots are not very good at summarization, rather than a few keywords, they do not spit out sentences in the right order. Introduction: Data-driven transformation (DTT) and Internet of Things (IoT) technologies have revolutionized the way businesses operate, enabling real-time monitoring and analysis of inventory systems. The prediction algorithms proved highly accurate forecasting demand with over 90% accuracy and delivered significant cost-savings in warehousing operations. Thus, including these two technologies combined certainly provides the best of from both worlds for the most accurate and efficient inventory control, that will lead to reducing operational inefficiencies, and can improve warehouse management significantly; thus making supply chains more
competitive in terms of cost and agility.