Proactive return management in E-commerce supply chains: Predictive analytics approach

In e-commerce, product returns management has become a critical concern for online retailers. With the drastic growth of online shopping, the increasing volume of returns poses significant challenges to the efficiency of supply chain operations. To effectively address these challenges, proactive return management strategies are essential. This MS thesis aims to predict product returns before the product is sold, leveraging open-source customer-item rating and feedback data. The study adopts a novel approach combining Natural Language Processing (NLP) techniques, Matrix Factorization based on Bayesian personalized ranking loss, and deep learning methodologies to uncover latent factors behind shopping and rating behavior to make accurate return predictions, aiding online retailers in proactive decision-making and optimizing their supply chain and inventory management processes.