Machine Learning for Demand Forecasting in Supply Chain Optimization

Demand forecasting is a crucial aspect of supply chain optimization, as it helps businesses anticipate future product needs and allocate resources efficiently. With the increasing complexity of global supply chains, traditional forecasting methods often fall short in handling large volumes of data and capturing intricate demand patterns. Machine learning (ML) techniques offer powerful tools to improve the accuracy of demand predictions by leveraging historical data, real-time information, and advanced algorithms. This paper explores the application of ML models such as regression analysis, time series forecasting, neural networks, and ensemble methods in demand forecasting. We discuss the key challenges, including data quality, model selection, and the integration of ML-driven forecasts into decision-making processes. The research highlights the potential of ML to enhance supply chain performance, reduce inventory costs, improve customer satisfaction, and increase operational efficiency.