AI-Powered Inventory Control System for Depot Management

AI-Powered Inventory Control System for Depot Management The AI-Powered Inventory Control System is an intelligent and data-driven solution designed to enhance depot-level inventory management using machine learning and predictive analytics. Traditional inventory systems rely on manual monitoring and static rules, which often lead to stock shortages, overstocking, delayed replenishment, and poor supplier coordination. The proposed system addresses these limitations by integrating real-time monitoring with AI-based forecasting and risk analysis. The system is built using a full-stack architecture with a MERN-based web platform for real-time inventory tracking and a Python-based machine learning engine for advanced analytics. It continuously collects and stores operational data such as stock levels, sales transactions, supplier records, and depot-wise inventory distribution in a centralized database. The core intelligence of the system lies in three major components: Demand Forecasting –Time-series forecasting using the ARIMA model predicts future product demand based on historical sales data. This helps managers plan replenishment in advance and reduce stockout risks. Inventory Status Classification –Machine learning models analyze stock levels, forecasted demand, lead times, and reorder thresholds to classify inventory conditions. This enables automatic identification of critical stock situations. Supplier Risk Assessment –The system evaluates supplier performance using metrics such as delivery delays, fulfillment reliability, and quality issues. Risk scores are generated to support better procurement decisions. The platform provides an interactive real-time dashboard displaying inventory health, depot utilization, transaction trends, forecast results, and supplier risk indicators. Automated alerts are triggered when stock levels cross predefined thresholds. Additionally, AI-generated reports support auditing and strategic planning. By combining predictive analytics, automation, and real-time visualization, the proposed system transforms traditional inventory control into an intelligent, proactive, and scalable decision-support framework. It improves operational efficiency, reduces costs, enhances supply chain resilience, and enables data-driven management across multiple depots.