Abstract
This work investigates the application of machine learning to demand forecasting in Supply Chain Management. Using the M5 Forecasting-Accuracy dataset, we develop a forecasting pipeline comprising a baseline and an enhanced feature-rich variant. A unified framework is used to train and evaluate diverse forecasting approaches, including linear models, neural networks, and Transformer-based architectures, in both univariate and multivariate settings. Results highlight the importance of data representation. On the baseline dataset, classical methods such as ARIMA achieve the best performance in univariate forecasting, while Transformers perform poorly. On the enhanced dataset, TimesFM 1.0 attains the lowest mean absolute error across all horizons, with linear models and shallow RNNs providing competitive, computationally efficient alternatives. Additional covariates generally offer limited benefits and, for Transformer-based models, often lead to substantial performance degradation.
Paper Type
Poster
DOI
10.62036/ISD.2026.52
Comparison of Supply Chain Management Machine Learning Methods
This work investigates the application of machine learning to demand forecasting in Supply Chain Management. Using the M5 Forecasting-Accuracy dataset, we develop a forecasting pipeline comprising a baseline and an enhanced feature-rich variant. A unified framework is used to train and evaluate diverse forecasting approaches, including linear models, neural networks, and Transformer-based architectures, in both univariate and multivariate settings. Results highlight the importance of data representation. On the baseline dataset, classical methods such as ARIMA achieve the best performance in univariate forecasting, while Transformers perform poorly. On the enhanced dataset, TimesFM 1.0 attains the lowest mean absolute error across all horizons, with linear models and shallow RNNs providing competitive, computationally efficient alternatives. Additional covariates generally offer limited benefits and, for Transformer-based models, often lead to substantial performance degradation.
Recommended Citation
Tomaszewski, Ł. & Luckner, M.(2026). Comparison of Supply Chain Management Machine Learning Methods. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.52