With the rise of (smart) service systems, there are increasingly opportunities for collaboration and new ways of co-creating value. Every action in a service system produces data that can be a valuable source of information to trigger value co-creation. Although the analysis of this data is crucial for its economic success, fully exploiting its potential still remains one of the challenges for smart service systems. In our lab, we analyze and improve such situations where relevant data sources and analytical knowledge are typically controlled by different entities—but not distributed or shared among them.
We are interested in the utilization of machine learning and artificial intelligence for the development of innovative services. We follow the complete lifecycle of AI from business problem to deployment, e.g. in the industrial sector. By actually designing and implementing AI-based services, we uncover new theoretical and practical knowledge.
Current topics include
- AI in Service Systems
- Meta machine learning
- Transfer machine learning
- System-oriented service delivery
- AI for Industrial Services / Smart Services
- Predictive Maintenance Cyber-physical Systems
- Human-machine collaboration in services
- AI-based industrial services
- Validity of predictive services
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