Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model

With dual‐channel choices, E‐retailers fulfill their demands by either the inventory stored in third‐party distribution centers, or by in‐house inventory. In this article, using data from a wedding gown E‐retailer in China, we analyze the differences between two fulfillment choices—fulfillment by Amazon (FBA) and fulfillment by seller (FBS). In particular, we want to understand the impact of FBA that will bring to sales and profit, compared to FBS, and how the impact is related to product features such as sizes and colors. We develop a risk‐adjusted fulfillment model to address this problem, where the E‐retailer's risk attitude to FBA is incorporated. We denote the profit gaps between FBA and FBS as the rewards for this E‐retailer fulfilling products using FBA, our goal is to maximize the E‐retailer's total rewards using predictive analytics. We adopt the generalized linear model to predict the expected rewards, while controlling for the variability of the reward distribution. We apply our model on a set of real data, and develop an explicit decision rule that can be easily implemented in practice. The numerical experiments show that our interpretable decision rule can improve the E‐retailer's total rewards by more than 35%.