A Data Driven IOT Framework for Real-Time Inventory Tracking and Predictive Management

The increasing demand for efficient inventory management has made manual stock tracking difficult and time-consuming. This paper proposes a data-driven IoT framework for real-time inventory tracking and predictive management. The system monitors inventory data, detects stock abnormalities, and generates alerts when stock levels require attention. Machine Learning (ML) techniques are used to analyze historical inventory data and predict future stock requirements. Keypoints — Internet of Things, Inventory Management, Real-Time Tracking, Machine Learning, Predictive Analytics