Abstract: This application relates to employing trained machine learning processes to determine item substitutions, such as item substitutions for low-velocity items. For example, a computing device may generate features based on item data for a pair of low-velocity items. The computing device may apply a trained machine learning process to the generated features to determine a substitution score between the pair of low-velocity items. In some examples, and based on the substitution scores, the computing device may rank the low-velocity items. The computing device may receive a request for substitute items for one of the low-velocity items, and may transmit an indication of one or more of the other low-velocity items based on the ranking. In some examples, the computing device trains the machine learning process based on item data for high-velocity items and substitute scores between the high-velocity items.

Abstract: A system for determining recommended product assortments for a retail environment includes at least one computing device that obtains store layout data, product data and forecast data. The forecast data characterizes projected sales information for the products described in the product data. The computing device also obtains demand transference data characterizing changes in demand for one or more products when a different product is unavailable and obtains product replenishment data characterizing a cost to re-stock products. The computing device also determines a recommended product assortment for the products described in the product data for each store described in the store layout data based on the forecast data, the demand transference data, and the product replenishment data and then displays the recommended product assortment.