AI-BASED OPTIMIZED SUPPLY CHAIN MANAGEMENT SYSTEM

Abstract

In today’s competitive environment, small local shops such as bakeries, groceries, clothing, and stationery stores face challenges in managing inventory, meeting customer demand, and avoiding inefficiencies like stockouts or overstocking. Traditional supply chain practices are largely manual, error-prone, and lack predictive capabilities, while advanced frameworks used in large enterprises are too complex and resource-intensive to be applied in neighbourhood businesses. This gap results in missed sales opportunities, higher costs, and limited resilience, highlighting the need for a practical, lightweight solution tailored to small shops. The proposed “AI-Based Optimized Supply Chain Management System” addresses these limitations by integrating role-based dashboards, real-time notifications, and intelligent analytics into a single platform. Customers can browse shops by category, view products, and place orders online, while shop owners manage stock, track orders, and receive alerts for low inventory. The system leverages algorithms for demand forecasting, reorder point calculation, and profitability analysis, ensuring efficient resource utilization and improved customer satisfaction. By combining predictive analytics with an accessible interface, the system delivers the benefits of modern supply chain optimization in a form that is scalable, resilient, and community-focused. This work demonstrates how artificial intelligence can be applied to create smarter, sustainable supply chain practices for small businesses, bridging the gap between traditional shop keeping and digital commerce.