IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-49979-1_2.html
   My bibliography  Save this book chapter

Fast Fashion’s Fate: Artificial Intelligence, Sustainability, and the Apparel Industry

In: Artificial Intelligence for Sustainability

Author

Listed:
  • Andreas Kaplan

    (KLU - Kühne Logistics University)

Abstract

The clothing sector is one of the biggest polluters in the world. Aware of the growing number of environmentally conscious consumers, several fashion brands aim to become more sustainable. Artificial intelligence (AI), defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” may be applied to fast fashion as a means of greening the apparel industry. This chapter explains how AI can enhance the sustainable production and consumption of clothing products. First, it provides an overview of AI and analyzes and decodes its potential and associated risks and challenges. Numerous examples describe AI’s application to the retail and clothing industries, such as supply chain optimization and fostering eco-responsible consumption patterns. Second, this chapter illustrates how AI can help the fashion industry significantly reduce its carbon footprint. Third, three case studies of fashion companies that have started implementing artificial intelligence into their operations to improve sustainability are put forward, including two fast-fashion companies (H&M and Zara) and one luxury fashion retail platform (Farfetch). Finally, the chapter concludes with suggestions for the future of fast fashion.

Suggested Citation

  • Andreas Kaplan, 2024. "Fast Fashion’s Fate: Artificial Intelligence, Sustainability, and the Apparel Industry," Springer Books, in: Thomas Walker & Stefan Wendt & Sherif Goubran & Tyler Schwartz (ed.), Artificial Intelligence for Sustainability, chapter 2, pages 13-30, Springer.
  • Handle: RePEc:spr:sprchp:978-3-031-49979-1_2
    DOI: 10.1007/978-3-031-49979-1_2
    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:978-3-031-49979-1_2. 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.