The main objectives of the Full Professor of Data-driven Maintenance Management and Research Group of Production and Maintenance Management (PIM) is to

  • conduct fundamental research on “Innovative Data-driven and Technology-enhanced methodologies and approaches” conform to “Automated and Sustainable Production and Maintenance Management” in machine, physical asset, shop floor, factory and supply chain level
  • carry out applied-oriented research to develop, optimize, evaluate, validate and transfer digital technologies and approaches to industries, and further
  • take part in practice-oriented educational research on “shaping human dimension in production and maintenance” from work-based learning and competence management perspectives, gaining benefits from network of learning factories (in particular IALF) and knowledge management methodologies.

Hence, the professorship and PIM research group should bridge the gap between fundamental and applied-oriented research, facilitate industrial innovation and sustain human-centricity in maintenance engineering, production and asset management, especially through joint partnership and cross-disciplinary research with other chairs, faculties, universities and industries in Austria, in DACH, EU and beyond.

Enablers and Tools

In this context, several data-driven methods and technologies as “Enablers” are foreseen, namely:

  • AI-driven, and (physics-informed) knowledge exploration and data semantification for discovering and protecting knowledge from multimodal and multi-structured data sources, and creating knowledge-bases linked to various phases of production and maintenance management,
  • Simulation and Modeling for establishing Digital Twins for Production and Maintenance Planning and utilizing predictive and prescriptive analytic methods to improve quality of planning and reliability of decision-making processes, and
  • Process modeling and -mining for modeling reliability-centered processes, improving process efficiency, and thus understanding dynamics of production and maintenance planning.


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