Abstract
With the current technology development and the rapid rise of machine learning and forecasting algorithms, the supply chain evolves in a different era. In this paper, a forecasting approach is proposed, which has a strong capability of predicting the future volume size of products and customers. Therefore, in this paper, we propose a volume forecasting method that is compared with some well-known time series forecasting techniques from both statistical boosting methods using a specific dataset. These methods include LightGBM, XGBoost gradient boosting decision three algorithms, and Prophet algorithm. The experimental results indicate that RMSE, MAE, and R2 scores of products and customers trained by XGBoost, LightGBM, and Prophet, an interface that shows how the best model is chosen with statistical methods and how the three different algorithms forecast next day’s or next week’s volume efficiently.
Supported by DHL Supply Chain Turkey.
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Oruc, F., Yildirim, I., Cidal, G. (2023). Volume Forecasting in Supply Chain: A Mixed Study of Boosting and Prophet Algorithms. In: García Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds) Computational Intelligence, Data Analytics and Applications. ICCIDA 2022. Lecture Notes in Networks and Systems, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-27099-4_30
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DOI: https://doi.org/10.1007/978-3-031-27099-4_30
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