A general method for addressing forecasting uncertainty in inventory models

Dennis Prak*, Ruud Teunter

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

28 Citaten (Scopus)
279 Downloads (Pure)


In practice, inventory decisions depend heavily on demand forecasts, but the literature typically assumes that demand distributions are known. This means that estimates are substituted directly for the unknown parameters, leading to insufficient safety stocks, stock-outs, low service, and high costs. We propose a framework for addressing this estimation uncertainty that is applicable to any inventory model, demand distribution, and parameter estimator. The estimation errors are modeled and a predictive lead time demand distribution obtained, which is then substituted into the inventory model. We illustrate this framework for several different demand models. When the estimates are based on ten observations, the relative savings are typically between 10% and 30% for mean-stationary demand. However, the savings are larger when the estimates are based on fewer observations, when backorders are costlier, or when the lead time is longer. In the presence of a trend, the savings are between 50% and 80% for several scenarios. (C) 2017 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Originele taal-2English
Pagina's (van-tot)224-238
Aantal pagina's15
TijdschriftInternational Journal of Forecasting
Nummer van het tijdschrift1
StatusPublished - 2019

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