Authors : G Punith Sai; G Nagavallika; A V S Sai Babu; A Satish; Y Vinay Kumar; P Sunny Jaswanth; Ch Venkatesh

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/5n9amk9u

Scribd : https://tinyurl.com/3k3j9hdu

DOI : https://doi.org/10.38124/ijisrt/25apr681

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This project involves the development of an AI-driven inventory management system designed to simplify stock tracking and restocking for small businesses. It combines traditional inventory methods for products with stable demand and a machine learning model to predict restocking needs for items with fluctuating demand. The machine learning model is pre-trained on standard datasets, ensuring accurate forecasts without requiring training from user data. Developed using Django, MySQL, and Bootstrap, the system is web-based and accessible from any device. Key features include vendor management, automated restocking alerts via email, and a billing module for managing in-store sales. Users can categorize products, track stock levels in real time, and view a dashboard that highlights low-stock items. With a user- friendly interface and intelligent automation, this system supports small business owners in making efficient, data-driven decisions.

References :

1. Shamita Deshmukh, Asst. Prof. Sana Tak (2022). Inventory Management System. Journal of Scientific Research & Engineering Trends.
2. Caylı, O., & Oralhan, Z. (2024). Artificial Intelligence-Driven Inventory Management: Optimizing Stock Levels and Reducing Costs Through Advanced Machine Learning Techniques.
3. J.B. Munyaka & V.S.S. Yadavalli (2022). Inventory Management Concepts and Implementations. South African Journal of Industrial Engineering.