International Journal of Scientific Research and Engineering Development

Managing inventory effectively is one of the most operationally critical challenges facing small and medium enterprises (SMEs) today. Conventional approaches — manual stock registers, spreadsheets, and disconnected software — are fundamentally ill-suited to the demands of modern commerce, offering no real-time visibility, no access control, and no analytical intelligence. This paper presents the Inventory Analytics System (IAS), a comprehensive full-stack web application that digitises, automates, and intelligently augments the complete lifecycle of inventory management. The system is engineered using React.js for the presentation layer, Python Flask for the application logic layer, and SQLite for relational data persistence, following a modular three-tier architecture. A defining innovation of this work is the direct integration of artificial intelligence into operational workflows: an AIpowered price suggestion engine analyses procurement costs and historical sales velocity to recommend optimal selling prices, while a natural language chatbot interface allows users of all technical backgrounds to query live inventory data conversationally. Financial accuracy is maintained through a rigorously implemented First In, First Out (FIFO) batch costing mechanism that traces every sale back to its precise procurement batch. Role-based access control enforces security across three distinct user roles — Admin, Inventory Manager, and Staff — through JWTbased authentication. The system additionally supports automated PDF report generation, internal inter-staff messaging, real-time low-stock notifications, and session auto-logout. Evaluation confirms a 97% functional test pass rate, a 93% reduction in inventory discrepancy rates, and a System Usability Scale score of 82.4, collectively validating the system as a deployable, enterprise-grade solution for SME inventory intelligence.

Mrs.M.Vasuki, Dr.T.Amalraj Victoire, S.Sivaranjani,"Inventory Analytics System for Small and Medium Enterprises: An AI-Augmented Full-Stack Web Application for Real-Time Stock Intelligence" International Journal of Scientific Research and Engineering Development, V9(3): Page(2058-2066) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.