IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-662-43871-8_47.html
   My bibliography  Save this book chapter

Supply Chain Collaborative Forecasting Modeling

In: Liss 2014

Author

Listed:
  • Wenjie Wang

    (Donghua University)

  • Qi Xu

    (Donghua University)

  • Changchun Gao

    (Donghua University)

  • Xiaodong Liu

    (Donghua University)

Abstract

With cooperation among the partners, the supply chain can coordinate its operations and improve the efficiency. The cooperated partners could collaboratively forecast demand and replenish product along the supply chain under the collaborative planning framework. The collaborative forecasting method studied is based on the Bayesian combination model in this paper. The collaborative forecasting model simulation is implemented using the actual order data of a retail item shared among the supply chain partners. The collaborative model is combined with three single forecasting methods, which include the simple moving average, the exponential smoothing and ARIMA methods. The simulation results show the effectiveness of collaborative forecasting method and improvement of forecasting accuracy in the supply chain.

Suggested Citation

  • Wenjie Wang & Qi Xu & Changchun Gao & Xiaodong Liu, 2015. "Supply Chain Collaborative Forecasting Modeling," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 317-322, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-43871-8_47
    DOI: 10.1007/978-3-662-43871-8_47
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-3-662-43871-8_47. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.