As simple as possible but no simpler - Research

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/proceeding › Article in proceedings › Research › peer-review

Standard

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

2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). New York, NY : IEEE, 2018. p. 153-157 8607312.

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

Harvard

Thorstenson, A 2018, As simple as possible but no simpler: An inquiry into approximations for a re-order point inventory control model with gamma-distributed demand. in 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)., 8607312, IEEE, New York, NY, pp. 153-157, 2018 IEEE International Conference on Industrial Engineering & Engineering Management, Bangkok, Thailand, 16/12/2018. https://doi.org/10.1109/IEEM.2018.8607312

Vancouver

Thorstenson A. As simple as possible but no simpler: An inquiry into approximations for a re-order point inventory control model with gamma-distributed demand. In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). New York, NY: IEEE. 2018. p. 153-157. 8607312 doi: 10.1109/IEEM.2018.8607312

Author

Thorstenson, Anders. / As simple as possible but no simpler : An inquiry into approximations for a re-order point inventory control model with gamma-distributed demand. 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). New York, NY : IEEE, 2018. pp. 153-157

Bibtex

@inproceedings{d9818a7c5b6c4f059592e5dce0c07fcc,

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

abstract = "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.",

keywords = "Backordering, continuous review, fill rate, gamma distribution, normal distribution, safety stock, stochastic demand",

author = "Anders Thorstenson",

year = "2018",

doi = "10.1109/IEEM.2018.8607312",

language = "English",

isbn = "978-1-5386-6785-9",

pages = "153--157",

booktitle = "2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)",

publisher = "IEEE",

note = "null ; Conference date: 16-12-2018 Through 19-12-2018",

}

RIS

TY - GEN

T1 - As simple as possible but no simpler

AU - Thorstenson, Anders

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Backordering

KW - continuous review

KW - fill rate

KW - gamma distribution

KW - normal distribution

KW - safety stock

KW - stochastic demand

UR - http://www.scopus.com/inward/record.url?scp=85061776354&partnerID=8YFLogxK

U2 - 10.1109/IEEM.2018.8607312

DO - 10.1109/IEEM.2018.8607312

M3 - Article in proceedings

SN - 978-1-5386-6785-9

SP - 153

EP - 157

BT - 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

PB - IEEE

CY - New York, NY

Y2 - 16 December 2018 through 19 December 2018

ER -

Department of Economics and Business Economics