Authors:
Marie Niederlaender
;
Aena Lodi
;
Soeren Gry
;
Rajarshi Biswas
and
Dirk Werth
Affiliation:
August-Wilhelm Scheer Institut, Uni Campus D 5 1, Saarbrücken, Germany
Keyword(s):
Returns Prediction, Machine Learning, Recommendation System, Sustainable Return Management, E-Commerce, Fashion, Apparel, Artificial Intelligence.
Abstract:
Product returns are an increasing burden for manufacturers and online retailers across the globe, both economically and ecologically. Especially in the textile and fashion industry, on average more than half of the ordered products are being returned. The first step towards reducing returns and being able to process unavoidable returns effectively, is the reliable prediction of upcoming returns at the time of order, allowing to estimate inventory risk and to plan the next steps to be taken to resell and avoid destruction of the garments. This study explores the potential of 5 different Machine Learning Algorithms combined with regualised target encoding for categorical features to predict returns of a German online retailer, exclusively selling festive dresses and garments for special occasions, where a balanced accuracy of up to 0.86 can be reached even for newly introduced products, if historical data on customer behavior is available. This work aims to be extended towards an AI-ba
sed recommendation system to find the ecologically and economically best processing strategy for garment returns to reduce waste and the financial burden on retailers.
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