A Metric for Assessing, Comparing, and Predicting the Performance of Autonomous RFID-Based Inventory Robots for Retail

Abstract

Radio frequency identification (RFID) technology is being widely adopted by retailers due to its accuracy, versatility, and reduction of operational costs. Most commonly, RFID in retail is used for taking frequent and accurate inventories of items in the stores. Usually, RFID inventories use handheld RFID devices, which makes the task tedious, costly, and prone to human errors. More reliable, fully automatic alternatives exist, such as smart shelves, overhead RFID antennas, and RFID-equipped robots. Among them, robots seem to be the preferred choice by retailers with large stores. However, retailers need an objective way to compare the different options for inventory solutions and to calculate the return on investment of each of them before they make an investment decision. In this article, we present a metric for assessing, comparing, and predicting the performance of autonomous RFID-based robots in retail stores. The metric is based on a theoretical model of both the store and the robot, and predicts the performance of a given robot when inventorying a specific store. The metric also allows to compare the performance of different RFID robots in different stores. The metric has been developed using experimental data and has been validated in a real store.