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Authors: Soeren Gry ; Marie Niederlaender and Dirk Werth

Affiliation: August-Wilhelm Scheer Institut, Uni Campus D 5 1, Saarbrücken, Germany

Keyword(s): Returns Prediction, Returns Prevention, Survey Results, Expert Interviews, Fashion E-Commerce, Recommendation System, Machine Learning, Sustainable Return Management, Sustainable Supply Chain.

Abstract: The fashion industry is one of the most problematic sectors in terms of sustainability. The fashion e-commerce sector is experiencing a surge in sales, which is leading to a significant increase in returns. This, in turn, is placing a considerable burden on the environment. High transport volumes or even the destruction of garments through returns pose major environmental and also economic problems. This study is based on a survey and expert interviews with decision-makers from the fashion industry. It provides indications of how an AI-based prediction and recommendation system could be used to avoid returns and manage them in an ecologically and economically sensible way. On the one hand, use cases are discussed that can be applied in the webshop system before the customer places an order, and on the other hand, ways are shown how returns predictions can support planning in the reverse logistics network.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Gry, S.; Niederlaender, M. and Werth, D. (2024). Enhancing Returns Management in Fashion E-Commerce: Industry Insights on AI-Based Prediction and Recommendation Systems. In Proceedings of the 21st International Conference on Smart Business Technologies - ICSBT; ISBN 978-989-758-710-8; ISSN 2184-772X, SciTePress, pages 66-73. DOI: 10.5220/0012759900003764

@conference{icsbt24,
author={Soeren Gry. and Marie Niederlaender. and Dirk Werth.},
title={Enhancing Returns Management in Fashion E-Commerce: Industry Insights on AI-Based Prediction and Recommendation Systems},
booktitle={Proceedings of the 21st International Conference on Smart Business Technologies - ICSBT},
year={2024},
pages={66-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012759900003764},
isbn={978-989-758-710-8},
issn={2184-772X},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Smart Business Technologies - ICSBT
TI - Enhancing Returns Management in Fashion E-Commerce: Industry Insights on AI-Based Prediction and Recommendation Systems
SN - 978-989-758-710-8
IS - 2184-772X
AU - Gry, S.
AU - Niederlaender, M.
AU - Werth, D.
PY - 2024
SP - 66
EP - 73
DO - 10.5220/0012759900003764
PB - SciTePress