Supply Chain Demand Forecasting with Machine Learning and Feature Engineering

Abstract

Supply chain demand forecasting is a critical component of supply chain management, enabling businesses to anticipate future demand, optimize inventory, reduce costs, and improve customer satisfaction. Traditional forecasting methods, such as time series analysis and econometric models, often fail to capture the complexities of modern supply chain dynamics. The emergence of machine learning (ML) and feature engineering techniques has revolutionized demand forecasting by leveraging vast amounts of data to identify patterns and trends that were previously undetectable. This research explores the application of ML models and feature engineering in demand forecasting, emphasizing the role of data preprocessing, feature selection, and model optimization. The study presents an empirical analysis comparing various ML algorithms, including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, to evaluate their effectiveness in demand forecasting. Experimental results demonstrate that advanced ML models, combined with robust feature engineering, significantly outperform traditional forecasting techniques. The research highlights the challenges, benefits, and future potential of integrating ML-driven forecasting into supply chain management.