Identifying and Analyzing Data Model Requirements and Technology Potentials of Machine Learning Systems in the Manufacturing Industry of the Future

Although machine learning (ML) methods have already been well described in science, the transfer into manufacturing business practice is only slowly taking place. One of the reasons is that current research is lacking a comprehensive analysis of working ML methods and their characteristics. Therefore, this paper systematically analyzes successfully implemented ML solutions to facilitate the design process for future machine learning systems (MLS) implementations in manufacturing companies. First, a systematic literature review based on 18 scientific publications is conducted to confirm the lacks assumed. Second, 15 MLS approaches are analyzed based on a technology framework to solve the shortcomings identified and extract further findings. In total, we identified two general MLS design patterns. Furthermore, we extracted seven suitable data and data model requirements as well as technology potentials. The results show that theory-based ML approaches are often based on linear methods requiring low-dimensional data, e.g. in image recognition. This points towards the hypothesis, that the application of non-linear ML methods processing high-dimensional data could increase the number of possible use cases. Thus, further high technology potentials regarding the application of MLS in the manufacturing industry would arise.