The problem of products missing from the shelf is a major one in the grocery retail sector, as it leads to lost sales and decreased consumer loyalty. Yet, the possibilities for detecting and measuring an out of shelf situation are limited, mainly conducted via a visual shelf check. The existence of a method for detecting the products that are not on the shelf based on sales data would be valuable, offering an accurate view of the shelf availability both to retailer and the product suppliers. In this paper, we suggest a method based on the employment of machine learning techniques, in order to develop a rule based system. Results up to now presents that rules related with the detection of out of the shelf products are characterized by acceptable levels of accuracy.
Papakiriakopoulos, Dimitris, "Building Classifiers For Detecting Products Missing From The Shelf" (2009). MCIS 2009 Proceedings. 85.