Finding Misplaced Items in Retail by Clustering RFID Data

摘要

In retail, products are organized according to layout plans, so-called planograms. Compliance to planograms is important, since good product placement can significantly increase sales. Currently, retailers are about to implement RFID installations consisting of smart shelves and RFID-tagged items to support in-store logistics and processes. In principle, they can also use these installations to implement plan-ogram compliance verification: Each antenna is supposed to detect all tagged items in one location of the planogram. But due to physical constraints, RFID tags can be identified by more than one RFID antenna. Thus, one cannot decide if an item carrying such a tag complies with the planogram. We propose a new method called RPCV which checks planogram compliance on large databases of items. It is based on the observation that the number of times an antenna identifies each item of a certain product type roughly follows a normal distribution. RPCV represents each item as a two-dimensional vector containing the number of readings both by the right antenna and by wrong ones according to the planogram. It clusters this data, separately for each product type. A cluster then is a set of correctly placed items or of misplaced ones. RPCV produces one order of magnitude less wrong predictions than current state of the art, and it requires less data to yield good predictions. A study with RFID-equipped goods and smart shelves shows that our approach is effective in realistic scenarios.