IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/738675.html
   My bibliography  Save this article

Sales Forecasting for Fashion Retailing Service Industry: A Review

Author

Listed:
  • Na Liu
  • Shuyun Ren
  • Tsan-Ming Choi
  • Chi-Leung Hui
  • Sau-Fun Ng

Abstract

Sales forecasting is crucial for many retail operations. It is especially critical for the fashion retailing service industry in which product demand is very volatile and product’s life cycle is short. This paper conducts a comprehensive literature review and selects a set of papers in the literature on fashion retail sales forecasting. The advantages and the drawbacks of different kinds of analytical methods for fashion retail sales forecasting are examined. The evolution of the respective forecasting methods over the past 15 years is revealed. Issues related to real-world applications of the fashion retail sales forecasting models and important future research directions are discussed.

Suggested Citation

  • Na Liu & Shuyun Ren & Tsan-Ming Choi & Chi-Leung Hui & Sau-Fun Ng, 2013. "Sales Forecasting for Fashion Retailing Service Industry: A Review," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    • Handle: RePEc:hin:jnlmpe:738675
    DOI: 10.1155/2013/738675
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2013/738675.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2013/738675.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/738675?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
    ---><---

    Citations

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


    Cited by:

    1. Yajaira Cardona-Valdés & Samuel Nucamendi-Guillén & Rodrigo E. Peimbert-García & Gustavo Macedo-Barragán & Eduardo Díaz-Medina, 2020. "A New Formulation for the Capacitated Lot Sizing Problem with Batch Ordering Allowing Shortages," Mathematics, MDPI, vol. 8(6), pages 1-16, June.
    2. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    3. Giovanni Battista Gardino & Rosa Meo & Giuseppe Craparotta, 2021. "Multi-view Latent Learning Applied to Fashion Industry," Information Systems Frontiers, Springer, vol. 23(1), pages 53-69, February.

    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:hin:jnlmpe:738675. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.