IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v46y2014i1p55-66.html
   My bibliography  Save this article

Modeling the evolution of dependency between demands, with application to inventory planning

Author

Listed:
  • Amirhosein Norouzi
  • Reha Uzsoy

Abstract

This article shows that the progressive realization of uncertain demands across successive discrete time periods through additive or multiplicative forecast updates results in the evolution of the conditional covariance of demand in addition to its conditional mean. A dynamic inventory model with forecast updates is used to illustrate the application of the proposed method. It is shown that the optimal inventory policy depends on conditional covariances, and a model without information updates is used to quantify the benefit of using the available forecast information in the presence of additive forecast updates. The proposed approach yields significant reductions in system costs and is applicable to a wide range of production and inventory models. It is also shown that the proposed approach can be extended to the case of multiplicative forecast updates and directions for future work are suggested.

Suggested Citation

  • Amirhosein Norouzi & Reha Uzsoy, 2014. "Modeling the evolution of dependency between demands, with application to inventory planning," IISE Transactions, Taylor & Francis Journals, vol. 46(1), pages 55-66.
  • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:1:p:55-66
    DOI: 10.1080/0740817X.2013.803637
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0740817X.2013.803637
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/0740817X.2013.803637?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alexandre Forel & Martin Grunow, 2023. "Dynamic stochastic lot sizing with forecast evolution in rolling‐horizon planning," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 449-468, February.
    2. Dehaybe, Henri & Catanzaro, Daniele & Chevalier, Philippe, 2024. "Deep Reinforcement Learning for inventory optimization with non-stationary uncertain demand," European Journal of Operational Research, Elsevier, vol. 314(2), pages 433-445.
    3. Pinçe, Çerağ & Yücesan, Enver & Bhaskara, Prithveesha Govinda, 2021. "Accurate response in agricultural supply chains," Omega, Elsevier, vol. 100(C).
    4. Xiang, Mengyuan & Rossi, Roberto & Martin-Barragan, Belen & Tarim, S. Armagan, 2023. "A mathematical programming-based solution method for the nonstationary inventory problem under correlated demand," European Journal of Operational Research, Elsevier, vol. 304(2), pages 515-524.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:uiiexx:v:46:y:2014:i:1:p:55-66. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.