StockSense: An intelligent inventory management system for retail stores using machine learning-based demand forecasting

The retail sector, particularly small and medium-sized enterprises (SMEs), faces challenges such as inefficient inventory management, stock imbalances, and product wastage. Traditional methods lack real-time visibility and fail to support data-driven decision-making. This project aims to develop an Intelligent Inventory Management System for Retail Stores that improves operational efficiency and reduces losses through automation and predictive insights.The proposed system is a web-based application that integrates a Point of Sale (POS) interface for real-time inventory updates. It incorporates Machine Learning-based demand forecasting to analyze historical sales data and predict future product requirements. Additionally, a smart expiry management module monitors product shelf life and generates alerts for near-expiry items, enabling timely actions such as discounts to minimize wastage. The system also provides dashboards and reports to support better decision-making.The results demonstrate improved accuracy in inventory tracking, reduced stock-outs, and effective management of perishable goods. By automating key processes and integrating predictive analytics, the system enhances efficiency and reduces manual effort.