BUSINESS PROCESS REDESIGN FOR REDUCING UNDELIVERED PRODUCT RETURN LOSSES IN E-COMMERCE – AN EXPLAINABLE AI APPROACH

Product returns in e-commerce affect the profitability of the e-tailer. We adopt a two-stage approach to reduce undelivered product returns in an e-commerce firm. First, we develop and compare machine learning techniques—logistic regression, decision trees, Naïve Bayes, random forest, adaptive boosting, gradient boosting, stochastic gradient boosting, and deep neural networks—on their ability to predict undelivered returns. Next, we use explainable methods, such as relative importance and Shapley values, to develop insights from the best-performing machine learning model. Finally, we use these insights and the predictive model to redesign the firm’s order fulfillment and return processes. A Post-implementation evaluation of the system confirms the impact of XAI in reducing the undelivered product returns from 22.5% to 6.34%. The study illustrates how combining XAI with predictive modeling can drive the reengineering of business processes, ultimately reducing product returns.