IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-33-4359-7_69.html
   My bibliography  Save this book chapter

The New Data-Driven Newsvendor Problem with Service Level Constraint

In: Liss 2020

Author

Listed:
  • Yuqi Ye

    (Beijing Jiaotong University)

  • Xufeng Yang

    (Beijing Jiaotong University)

Abstract

In today’s data-rich world, decision makers can employ not only demand observations but also external explanatory variables (i.e. features) to solve the newsvendor problem without traditional demand distribution assumption, which has been drawing increasing attention and has derived so-called new data-driven approaches. Still in its infancy, this paper proposes an improved new data-driven method based on Sample Average Approximation and the nonparametric machine learning technique to solve the newsvendor problem with target service level constraint that is faced by the front distribution center of e-commerce enterprises. Then numerical experiments based on the real dataset of a large e-commerce enterprise are conducted to compare the performances implemented by our approach and those implemented by other well-established methods. We found that our approach can get lower surplus inventory levels while realizing higher service levels especially when the target service level is higher than 80%, which provides practical guidance for the inventory decision of the e-commerce enterprise’s front distribution center under the big data environment.

Suggested Citation

  • Yuqi Ye & Xufeng Yang, 2021. "The New Data-Driven Newsvendor Problem with Service Level Constraint," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 1009-1023, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_69
    DOI: 10.1007/978-981-33-4359-7_69
    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:978-981-33-4359-7_69. 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.