Abstract: A method for obtaining a computed expected facing count (CEFC), including obtaining a sequence of actual facing counts (AFCs) during a specified sample window for each of a plurality of stock keeping units (SKUs), wherein each SKU is associated with one of a plurality of products; identifying a subset of the sequence of AFCs as candidate restock events at which it is assumed the product associated with an SKU has been replenished in a shelving area since a previous AFC observation; selecting a set of restock events that are most likely to represent the intentional restock level (EFC) for that SKU; preparing a plurality of EFC lists, wherein each of said plurality of EFC lists contains the EFCs for all the SKUs in a specified shelf area; periodically updating the plurality of EFC lists, thereby obtaining a plurality of updated EFC lists; and using the plurality of updated EFC lists to compute at least one Key Performance Indicator (KPI) for at least one shelf area.

Abstract: A method is provided for training at least one classifier model used by an artificial intelligence (AI) system to recognize each of a set of objects and to assign each of the set of objects to a class.