Smart Predictions: Machine Learning for Demand Forecasting Review and analysis

Accurate demand forecasting is critical for improving supply chain operations in Logistics 4.0, although traditional statistical approaches struggle to represent the complex, non-linear trends in current demand data. This study analyzes machine learning (ML) applications in demand forecasting across industrial supply chains, evaluating 21 research from 2020 to 2024. We propose a novel classification framework that categorizes ML techniques by algorithm type (e.g., deep learning, ensemble methods) and data characteristics (e.g., volume, dimensionality),showing themes including the advantage of LSTM networks (Long Short-Term Memory) for high-volume, multivariate data. Our findings demonstrate deep learning methods minimize predicting errors compared to standard approaches, while computing needs may restrict implementation. This categorization gives supply chain practitioners a practical guidance to identify appropriate ML approaches, boosting efficiency and profitability in changing marketplaces.