Abstract
We present forecasting models based on extreme gradient boosting to predict demand using real-world data of a German intermediary company in the media sector. The data set comprised the daily demand of 196,767 products from three years (mid-2017 to mid-2020) and meta information for each product including product type affiliation. Models were trained separately for each product type either to predict the demand on group or product level. For the latter, training was in rolling format based on the last 12 weeks to then predict the product’s short-term demand one-week ahead. Performance, evaluated via the coefficient of determination, is especially precise for specific product types. Engineered features consisting of seasonal information, statistical indices, and general performing indices obtained via fuzzy c-means clustering over time improved the prediction. Especially, predictions for the upcoming week on product level are challenging but of high value for future business decisions regarding inventory planning and purchase orders.
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Notes
- 1.
Non-linear effects in the demand behavior are particularly challenging and can be explained by competition among suppliers, the bullwhip effect, and mismatch between supply and demand [7].
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Lingelbach, K., Lingelbach, Y., Otte, S., Bui, M., Künzell, T., Peissner, M. (2021). Demand Forecasting Using Ensemble Learning for Effective Scheduling of Logistic Orders. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_39
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