Paper Title
Types of Machine Learning Systems for the Connected, Adaptive Production

Due to the increasing digitalization and connection of production areas, more and more companies have large amounts of process and machine data at their disposal. Utilization of this data by machine learning systems (MLS) offers productivity potential and thus combines self-control (adaptivity) with increased economic efficiency. Yet, in industry the diffusion of MLS into practical applications has been slow and the potential is only being tapped in isolated cases. This can be traced back to a missing understanding of the technology-inherent operating principles of machine learning (ML). This paper draws upon the assumption that types of MLS can be formed to structure the domain of ML. This enables a detailed understanding of MLS and their technology-inherent operating principles in a completely new manner. Thus, a framework for forming types of MLS is proposed and five types of MLS are derived. Furthermore, each type of MLS is detailed by an analysis of real world use cases. Keywords - Machine Learning, Artificial Intelligence, Technology Management, Production Management