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 IoT 
sensors, 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 
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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.