Expo Talk Panel
Theater A

Amazon’s Retail Inventory is the outcome of numerous supply-chain systems that optimize stochastic input variables to fulfill customer demand and maximize profit, while being subject to a variety of internal (labor, storage) and external constraints. Historically, we’ve relied on week-long manual subjective deep-dives that rely on disconnected metrics to inspect supply-chain health, identify defects, and improve supply chain outcomes. These methods have proved to not be scalable and even disconnected from actual supply-chain behavior. Therefore, we developed an automated solution that can connect all these supply-chain systems and inputs to inventory. While the opportunity is clear, building an inventory attribution system that works effectively at the scale of Amazon’s supply chain is non-trivial. To solve for this, we developed a two staged algorithm that we discuss here. Stage 1 of the algorithm trains a large-scale ML model over a billion observations to approximate a complex stochastic programming algorithm that Amazon uses to make buying decisions. We developed a novel attribution algorithm that leverages the concept from Shapley values in game theory. The attributes from the algorithm not only follows efficiency, symmetry, linearity, and null player properties, it also jointly attributes to the variables in the case when variables are highly dependent and independent attribution is not desirable. This algorithm is used in production within Amazon to attribute impact of input changes in the buying system towards Amazon retail inventory changes and is used in everyday operations to drive inventory related actions.