IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v40y2024i1p247-267.html
   My bibliography  Save this article

Demand forecasting for fashion products: A systematic review

Author

Listed:
  • Swaminathan, Kritika
  • Venkitasubramony, Rakesh

Abstract

Fashion is one of the most challenging categories for forecasting demand. Our study provides a systematic literature review of the different forecasting techniques used in the fashion industry. Particular focus is given to advancements in artificial intelligence and machine learning methods for predicting the demand for fashion products. Carefully compiled literature is analyzed, and the papers are classified into qualitative, statistical, artificial intelligence (AI), and hybrid techniques based on the forecasting method adopted by researchers. Our review identifies the challenges in predicting demand, and concludes by providing future research directions.

Suggested Citation

  • Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:1:p:247-267
    DOI: 10.1016/j.ijforecast.2023.02.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207023000134
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2023.02.005?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.

    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:eee:intfor:v:40:y:2024:i:1:p:247-267. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.