Christina Liepold – Technical University of Munich, Allemagne
Online retail marketplaces such as Zalando or Farfetch provide advantages for both suppliers and consumers. While suppliers gain an additional retail channel, consumers benefit from large product ranges in a one-stop-shop setting, the convenience of comparing multiple retail goods, and buyer-friendly shipping and return policies. However, with the success of online retail marketplaces, the amount and impact of retail return shipments have also increased. For example, in the U.S., the percentage of returns among online retail sales increased from 10.6% to 20% in 2020, resulting in high financial and environmental costs. To mitigate these costs, we propose exploiting the distinct characteristics of retail marketplaces, namely the retail platform’s knowledge of global customer demand and the interconnections within the network of suppliers that sell through the platform. Using a unique dataset of retail marketplace transactions, we examine whether an auction mechanism can mitigate demand uncertainty and environmental externalities by establishing connections between suppliers through networked returns. We design an auction-based setting where suppliers bid on customer-issued returns within the marketplace to increase their welfare while reducing environmental externalities. Compared to an individual supplier, the platform can globally track where items have been sold and, thus, permanently update estimations about demand distributions. The platform may use this knowledge about geographical demand distributions to redirect returns to suppliers with a higher reselling likelihood. Based on the implementation of such networked returns, the suppliers may gain additional welfare by reselling the respective returns. The platform may reduce its environmental impact by decreasing the shipping distance compared to the original return shipments. We show that an auction-based system where suppliers bid on customer-issued returns reduces returns’ shipping distance by up to 88% and can consequently increase the resell value of returns on average by 5%. Overall, the auction-based setting for networked returns can mitigate the drawbacks of benevolent return policies of retail marketplaces.
Biography: Christina is a Ph.D. student and Research Assistant at the Professorship of Business Analytics & Intelligent Systems at TUM School of Management at the Technical University of Munich (TUM), Germany. At the Professorship, she is also Team Lead for the Area of Analytics and Supply Chains. Previously, she completed the Master and Bachelor of Science in Management and Technology at TUM with a research stay at CIRRELT in Montréal, Canada, during her Master’s Thesis. Her research focuses on auction theory and network optimization as well as their implementation within “as-a-Service” applications.
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