• Kanchan Gawade
• Disha Kale
• Akansha Gore
• Sakshi Gaikwad
• Prof. Dhumal S.S

Small and medium-sized businesses often face challenges in inventory management due to inaccurate demand estimation, overstocking, and stock shortages. This paper presents a Smart Inventory & Demand Prediction System designed to improve inventory management by analyzing historical sales data and predicting future product demand. The proposed system integrates a web-based inventory management application with machine learning techniques to support demand forecasting and stock replenishment decisions. The application manages product details, sales, purchases, suppliers, and inventory records. A machine learning model processes historical sales data to estimate future demand and provide recommendations for stock replenishment. The proposed system aims to reduce manual effort, improve inventory visibility, and support data-driven decision-making for small and medium-sized businesses. The system uses React.js for the frontend, Spring Boot for backend services, MySQL for database management, and Python-based machine learning for demand prediction. The effectiveness of the proposed system will be evaluated using appropriate forecasting metrics and functional testing.

Smart Inventory Management, Demand Forecasting, Machine Learning, Inventory Optimization, Sales Prediction, Small and Medium-sized Businesses

"Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.b722-b727, October-2026, Available :http://www.jetir.org/papers/JETIR2610172.pdf

"Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppb722-b727, October-2026, Available at : http://www.jetir.org/papers/JETIR2610172.pdf