Analytics for Cross‐Border E‐Commerce: Inventory Risk Management of an Online Fashion Retailer
研究针对中国时尚零售商在海外仓库的库存问题，提出两阶段数据驱动方法：先用随机森林分类盈利与非盈利商品，再预测需求并优化库存决策，平均成本降低20%。
ABSTRACT 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 ‐items are profitable to store in overseas warehouses, but ‐items are not; (ii) the demand levels of SKUs of ‐items are predicted. In the subsequent prescriptive analytics, models are proposed for optimizing inventory decisions related to ‐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%).