Department of Economics and Business Economics

As simple as possible but no simpler: An inquiry into approximations for a re-order point inventory control model with gamma-distributed demand

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

The basic single item, continuous review, reorder point inventory control model with given order quantities and a fixed lead time is considered. The objective is to minimize inventory holding cost for the safety stock by determining the reorder point subject to a fill-rate constraint. Several approximations have been suggested for this model. Many of them assume that demand during the lead time follows a normal distribution. In this paper, model approximations assuming normal distributions are contrasted with gamma distributed lead-time demand. It is confirmed numerically, that the simplest approximation is quite accurate under some fairly restrictive conditions. However, it is found that considerable errors can be expected when demand uncertainty is increased, especially when the gamma distribution is applied. Fortunately, the more precise model specification with only marginally increased complexity works quite well, also for the gamma distribution.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
Number of pages5
Place of publicationNew York, NY
PublisherIEEE
Publication year2018
Pages153-157
Article number8607312
ISBN (print)978-1-5386-6785-9
ISBN (Electronic)9781538667866
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Industrial Engineering & Engineering Management - Bangkok, Thailand
Duration: 16 Dec 201819 Dec 2018

Conference

Conference2018 IEEE International Conference on Industrial Engineering & Engineering Management
LandThailand
ByBangkok
Periode16/12/201819/12/2018

    Research areas

  • Backordering, continuous review, fill rate, gamma distribution, normal distribution, safety stock, stochastic demand

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