IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-39869-8_3.html
   My bibliography  Save this book chapter

Collaborative Planning Forecasting Replenishment Schemes in Apparel Supply Chain Systems: Cases and Research Opportunities

In: Intelligent Fashion Forecasting Systems: Models and Applications

Author

Listed:
  • Daisy Ka-Yee Ho

    (The Hong Kong Polytechnic University)

  • Tsan-Ming Choi

    (The Hong Kong Polytechnic University)

Abstract

Fashion apparel industry is dynamic, and is highly affected by the ever-changing market trend and consumer needs. With the constant change of consumers’ preferences, concepts such as fast fashion arise and become more and more dominant. Many of these concepts are made feasible only with a highly efficient and responsive supply chain. In this paper, collaborative planning, forecasting and replenishment (CPFR) schemes and various inventory management practices in apparel supply chains are explored. We first focus on studying an individual case on a garment manufacturer. We then investigate the American denim supply chain and reveal various inventory management practices and the role played by CPFR. Some future research directions are discussed.

Suggested Citation

  • Daisy Ka-Yee Ho & Tsan-Ming Choi, 2014. "Collaborative Planning Forecasting Replenishment Schemes in Apparel Supply Chain Systems: Cases and Research Opportunities," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 29-40, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-39869-8_3
    DOI: 10.1007/978-3-642-39869-8_3
    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.

    Citations

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


    Cited by:

    1. Shuyun Ren & Hau-Ling Chan & Pratibha Ram, 2017. "A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 335-355, October.

    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:978-3-642-39869-8_3. 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.