Authors:
Soeren Gry
;
Marie Niederlaender
;
Aena Lodi
;
Marcel Mutz
and
Dirk Werth
Affiliation:
August-Wilhelm Scheer Institut, Uni Campus D 51, Saarbrücken, Germany
Keyword(s):
Recommender System, Sustainable Return Management, Logistics, Sustainable Supply Chain, E-Commerce, Fashion, Apparel, Artificial Intelligence, Machine Learning.
Abstract:
The ever-increasing volume of returned garments not only represents a huge increase in costs for retailers or manufacturers and an inventory risk that is difficult to calculate, but also has a high environmental impact due to the destruction of the garments and the necessary logistics processes. Most of the existing solutions to these problems aim to eliminate returns altogether. However, many returns cannot be avoided, e.g. orders for a selection of products, repairs or quality-related returns. For this reason, this study explores the potential of AI to predict returns and make recommendations on how best to plan the reverse logistics network, resulting in environmental and economic benefits. To this end, an extensive literature review was conducted to capture the current state of research. Based on this, a conceptual approach for the development of an AI-based recommender system for the best possible handling of returns will be derived.