Retail Inventory Demand Forecasting using Machine Learning

Abstract

Retail inventory management is a critical aspect of any retail business, directly impacting profitability, customer satisfaction, and operational efficiency. Traditional methods of demand forecasting, often based on historical sales data and intuition, lack the accuracy needed to optimize inventory levels, leading to either stock outs or overstocking. This project explores the application of machine learning techniques to forecast retail inventory demand more accurately. By leveraging historical sales data, market trends, seasonal patterns, and external factors like promotions and holidays, the machine learning model can predict future inventory requirements. Algorithms such as machine learning are evaluated for their performance in predicting demand. The results demonstrate that machine learning models significantly improve demand forecasting accuracy compared to traditional methods, enabling retailers to maintain optimal inventory levels, reduce costs, and improve customer satisfaction.