🤖 AI Summary
This study addresses the computational bottleneck in estimating demand substitution effects within large-scale product assortments by proposing an efficient modeling approach based on a constrained logit framework. Building upon independent demand forecasts for individual products, the method incorporates inter-product substitution relationships to adjust predictions, thereby circumventing the need to model each assortment combination separately. The approach enables, for the first time, scalable and accurate estimation of demand transfer coefficients at the million-product scale, overcoming the efficiency limitations of traditional discrete choice models in large-scale settings. Empirical validation using multi-category, multi-store historical transaction data demonstrates significant improvements in demand forecasting accuracy under plausible substitution assumptions.
📝 Abstract
Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice model and using this model to determine the value of an objective function (i.e. expected demand) with respect to an assortment proposal. However, for large item universe with many categories, this approach can prove inefficient, needing a separate demand forecast for every possible item assortment. An alternate approach exists whereby we combine the efficiency of forecasting item demand independently, while at the same time applying adjustments to the independent forecasts that account for the relations between item demand and the availability of other similar items on the shelf.
Central to this approach is the estimation of Demand Transfer (DT) coefficients. These DT coefficients represent the percent of a particular target item's (item that the customer walked in the store to buy) demand that is redirected to each other item in the universe should the target item be removed from the shelf. We introduce an approach that allows us to compute these DT coefficients on large item universes (assortments having 1 million+ items). Experiments on data as well as historical transaction data for multiple locations within categories demonstrate that when certain reasonable assumptions about substitution behavior are satisfied, our procedure is able to accurately estimate underlying DT coefficients and lead to improvements in demand forecasting.