The digital transformation is not a purely technical phenomenon. Digital technologies are enablers who extend their full potential only in the light of the social and organizational changes that accompany them. Mastering this change through socio-technical transformation processes and providing suitable interdisciplinary methods and processes is the goal of our research.

Strategical capturing and analysis of industrial data is becoming increasingly important. This goes along with a multitude of open questions regarding their development and utilization. The design of suitable architectures such as Industrial Data Lakes and Data Spaces, but also their sustainable implementation and use still hold massive potential. We develop ways to successfully master this challenge in the long term.

Digital technology leads to new network architectures and forms of communication between man and machine. Not only the technical networks undergo fundamental changes to cyber-physical systems, but also human integration is changing. Collaborative tasks require new concepts of human-machine interaction that are explored in this research area.

Machine learning is considered one of the cornerstones of Industry 4.0. On the one hand, the data-driven learning methods promise added value from industrial measurements, for example in smart health monitoring of machines or the prediction of maintenance requirements. On the other hand, reinforcement approaches allow learning from mistakes and the deduction of solutions to previously unknown scenarios. To explore the manifold potentials here is our goal.

Logistics for transport between and in production facilities is increasingly complex and decentralized due to developments towards "lot size 1" and individualized products. This is accompanied by a decoupling of suppliers for production and delivery. We develop intelligent (partially) autonomous transport systems not least on the basis of our findings from four world championship titles in the RoboCup Logistics League.

At one of the top training places in engineering, we are committed to using new technology and methods to improve the quality of teaching. Together with other departments and didactic facilities, we form a competence center for engineering education in which we not only implement virtual learning environments and remote labs, but also promote sustainable content such as entrepreneurial thinking.