Analytics for Cross-Border E-Commerce: Inventory Risk Management of an Online Fashion Retailer

摘要

Problem statement: We present a data-driven analytics study of a Chinese fashion retailer. The retailer fulfills cross-border orders using online platforms, but faces inventory problems in its overseas warehouses, owing to operational complexities, such as extensive product offerings, high demand risks, and tax risks in cross-border trade. Traditional approaches (e.g., model-driven approaches) often fail to provide effective solutions. Therefore, this study proposes a new data-driven approach to manage inventory in overseas warehouses. Methodology: A two-stage predictive analytics approach is implemented, as follows: (i) all items are classified into one of two classes, where A-items are profitable to store in overseas warehouses, but B-items are not; (ii) the demand levels of SKUs of A-items are predicted. In the subsequent prescriptive analytics, models are proposed for optimizing inventory decisions related to A-items. These include a deterministic model that uses the predicted demand as the true demand, and a stochastic model that treats the true demand as a random variable. Results: (i) Using a variety of machine learning techniques in the predictive analytics phase, we find the random forest outperforms other methods. (ii) The deterministic model can be solved as a linear program, and the stochastic model with maximum entropy distributions can be solved using Karush-Kuhn-Tucker conditions. (iii) An application of our results shows that the predictive classification reduces costs (an average cost reduction of up to 20%) by avoid shipping unprofitable items to overseas warehouses. Furthermore, the stochastic model provides near-optimal solutions (the smallest performance loss is just 0.00%).