A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing

Abstract

Users in manufacturing willing to apply machine-learning-based (ML-based) data analytics face challenges related to data quality or to the selection and configuration of proper ML algorithms. Current approaches are either purely empirical or reliant on technical data. This makes understanding and comparing candidate solutions difficult, and also ignores the way it impacts the real application problem. In this paper, we propose a framework to generate analytics solutions based on a systematic profiling of all aspects involved. With it, users can visually and systematically explore relevant alternatives for their specific scenario, and obtain recommendations in terms of costs, productivity, results quality, or execution time.