Knowledge management on the shop floor through recommender engines

Abstract

Even with digital shop floor management systems on the rise the knowledge about previously solved problems in production is not shared and used valuably. This paper aims to evaluate if recommender engines can be utilized in digital shop floor management systems to overcome this gap. Therefore, in a first step interviews are conducted to identify requirements from an industry perspective. Based on those, a system for problem solving management with an integrated content-based recommender engine to enhance knowledge management on the shop floor is designed, implemented and tested for its functionality. The evaluation of the quality of the recommendations as well as the quantified industry feedback give promising results and indicate future research and development steps.