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.
Yahya et al. (Mon,) studied this question.
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