Forecasting demand for slow-moving items in case of reporting errors

Abstract : The paper considers the problem of demand forecasting for slow-moving items in case of reporting errors. A generalization of the beta-binomial demand model is proposed that takes into account possible distortions in the learning sample. Properties of the underlying probability distribution are derived. For this new model, algorithms that provide consistent estimators of the model parameters as well as mean square error optimal forecasts when used for historical demand data with reporting errors are developed. An example for slow-moving car parts is given to illustrate the proposed demand forecasting approach.
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Article dans une revue
Risk and Decision Analysis, IOS, 2009, Volume 1 (Number 4), pp.Pages 221-230. 〈10.3233/RDA-2009-0019〉
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https://hal-emse.ccsd.cnrs.fr/emse-00676023
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Soumis le : vendredi 2 mars 2012 - 16:27:08
Dernière modification le : mardi 23 octobre 2018 - 14:36:08

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Alexandre Dolgui, Maksim Pashkevich. Forecasting demand for slow-moving items in case of reporting errors. Risk and Decision Analysis, IOS, 2009, Volume 1 (Number 4), pp.Pages 221-230. 〈10.3233/RDA-2009-0019〉. 〈emse-00676023〉

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