A data-driven and context-aware approach for demand forecasting in the beverage industry
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A data-driven and context-aware approach for demand forecasting in the beverage industry.pdf
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Author(s)
Ma, Benedict Jun
Jackson, Ilya
Huang, Maggie
Villegas, Sebastian
Macias-Aguayo, Jaime
Date Issued
October 10, 2025
Journal
International Journal of Logistics Research and Applications
Publisher
Taylor & Francis
Citation
Ma, B. J., Jackson, I., Huang, M., Villegas, S., & Macias-Aguayo, J. (2025). A data-driven and context-aware approach for demand forecasting in the beverage industry. International Journal of Logistics Research and Applications, 1–28.
Version
Final published version
Abstract
Accurate demand forecasting is essential for logistics and supply chain management as it enables efficient inventory planning, reduces operational costs, and ensures high service levels across the network. However, in practice, diverse demand patterns of items make this task challenging, and a one-size-fits-all forecasting approach is inadequate. This paper proposes a data-driven and context-aware forecasting framework and tests it by using both endogenous data from a large private-label beverage manufacturer and exogenous features (such as holidays and temperature). Our method begins by classifying SKUs based on demand volume, volatility, and intermittency, and then refining the derived clusters by taking volume distribution into account. Totally, we obtain four distinct clusters, which are (i) stable and high volume, (ii) stable with low volume, (iii) erratic and intermittent, and (iv) lumpy. To explore the appropriate forecasting models for different demand patterns, we employ statistical models (exponential smoothing, ARIMA, and Croston), machine learning models (XGBoost), deep learning models (TiDE and N-BEATS), and even qualitative approaches such as collaborative planning, forecasting, and replenishment (CPFR). Our experimental results suggest which forecasting models are recommended for each demand pattern, and insightful implications are provided for the managers.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/13675567.2025.2566806
