Application of Machine Learning in Demand Forecasting and Inventory Optimization

Abstract

Accurate demand forecasting and efficient inventory management are crucial for organizations to stay competitive in today’s fast-changing, data-driven business world. Traditional forecasting methods often miss complex demand patterns caused by seasonality, consumer behavior, promotions, and outside factors. Machine learning (ML) has changed how companies approach demand forecasting and inventory optimization by making decisions more data-driven, flexible, and predictive. This paper explores how machine learning is used in demand forecasting and inventory optimization, looking at both management and technology aspects. It reviews key ML algorithms, how they fit into supply chain systems, their benefits, challenges, and ethical issues. The paper also presents a framework showing how ML can improve forecast accuracy, lower inventory costs, and boost service levels. The findings show that ML can make supply chains more responsive and efficient, but success depends on good data, organizational readiness, and strong collaboration between people and machines. The paper ends with practical advice for managers and ideas for future research.