A general method for addressing forecasting uncertainty in inventory models

Dennis Prak*, Ruud Teunter

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
31 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)224-238
Number of pages15
JournalInternational Journal of Forecasting
Volume35
Issue number1
DOIs
Publication statusPublished - 2019

Keywords

  • Demand forecasting
  • Inventory control
  • Safety stock
  • Parameter uncertainty
  • Bayesian methods
  • DEMAND PARAMETERS

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