Abstract
Supply Chain Management relies heavily on forecasts, e.g. of future demand or future prices. Most applications, however, use static forecasting models in the sense that past data is used for model construction and evaluation without being updated adequately when new data becomes available.We propose a dynamic forecasting methodology and show its effectiveness in a real-world application.
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© 2008 Springer-Verlag Berlin Heidelberg
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Weber, R., Guajardo, J. (2008). Dynamic Data Mining for Improved Forecasting in Logistics and Supply Chain Management. In: Kreowski, HJ., Scholz-Reiter, B., Haasis, HD. (eds) Dynamics in Logistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76862-3_4
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DOI: https://doi.org/10.1007/978-3-540-76862-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76861-6
Online ISBN: 978-3-540-76862-3
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