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

Ye Shi · Ting Wang · Layth C. Alwan

57 citations0/0 files storedDecision Sciences

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Supply Chain and Inventory Management Forecasting Techniques and Applications Big Data and Business Intelligence

Abstract

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%).

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References

2000Optimization of conditional value-at-risk 2010Theory and Applications of Robust Optimization 2006A Robust Optimization Approach to Inventory Theory 2015Analytics for an Online Retailer: Demand Forecasting and Price Optimization 2018The Big Data Newsvendor: Practical Insights from Machine Learning 2003On the bullwhip and inventory variance produced by an ordering policy 2014The drivers and impediments for cross-border e-commerce in the EU 2005Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach

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