Optimizing Retail Demand Forecasting with Advanced Machine Learning Techniques

Abstract

Accurate demand forecasting is critical for retail businesses to optimize inventory management, reduce costs, and improve customer satisfaction. Traditional forecasting methods often struggle to capture the complexities of modern retail environments, which are influenced by factors such as seasonality, promotions, competition, and external shocks. This paper explores the application of machine learning techniques to enhance demand forecasting accuracy for retail businesses. By leveraging historical sales data, external factors, and customer behavior patterns, machine learning models such as XGboost, long short-term memory (LSTM), and random forest are compared in terms of performance and adaptability to changing market conditions. The study highlights the importance of feature engineering, hyperparameter tuning, and model selection in achieving robust forecasts. Real-world case studies and experiments demonstrate the significant improvement in forecast accuracy over traditional statistical methods, offering actionable insights for inventory planning and operational efficiency. The findings underscore the transformative potential of machine learning in addressing the dynamic challenges of retail demand forecasting, paving the way for smarter decision-making and competitive advantage.