Integration of Predictive Analytics into Inventory Management Systems Using Machine Learning

Abstract

Small and medium-sized retail businesses frequently face difficulties maintaining optimal inventory levels due to fluctuating demand, seasonality, and traditional manual decision making. This article proposes the design of an intelligent inventory management system integrating predictive analytics through machine learning techniques to forecast product demand and generate dynamic restocking recommendations. The system utilizes a cloud-hosted PostgreSQL database managed through Supabase, a FastAPI-based Python backend, and a React-based web interface. Forecasting models are developed using established time-series methodologies such as ARIMA, Prophet, and Long Short-Term Memory (LSTM) neural networks. The goal is to demonstrate how predictive analytics can enhance operational efficiency, reduce losses caused by inadequate inventory planning, and support data-driven decision-making in modern retail environments. Keywords – Cloud Databases, Inventory Management, Machine Learning, Predictive Analytics, Time-Series Forecasting.