The first line of research (AR@APPRISE) investigates barriers and enablers of using Augmented Reality (AR) to facilitate the industrial service function. AR may include many benefits for both machine manufacturers and machine users; however, it could also reduce revenue from traditional field services and even adversely affect spare part sales.
Therefore, a second line of research (BM@APPRISE) relates to the investigation and development of business models for AR-based services.
In a third line of research (ML@APPRISE) business models arising from machine learning are regarded. The monetisation of concepts such as condition monitoring, predictive maintenance and preventive maintenance is investigated.
Our research is conducted in cooperation with Professor Rakesh Mishra of the Centre for Efficiency and Performance Engineering (CEPE) at the University of Huddersfield and is internally funded by the Frankfurt University of Applied Sciences and by industry donations.
Within this research project we are investigating the effects of using Mobile Collaborative Augmented Reality on the industrial field service business (deployment of field service technicians, access to remote expert knowledge). The emerging technology is tested in case studies using head-mounted displays, such as Vuzix M300 or RealWear HTM-1 and head-held devices, such as smartphones and tablets. We are determining relevant business cases for practical use and the benefits for machine manufacturers and machine users resulting from these business cases. In addition, to the positive effects we are also identifying barriers and key activities to the implementation of AR-technology.
The research project AR@APPRISE is funded by industrial partners and internal funds provided by the Faculty of Computer Science and Engineering.
Contact: Maike Müller
Industry 4.0 is accompanied by novel technology such as Augmented Reality, and innovative business models such as platform ecosystems. On the one hand, these innovations are promising advantages for capital goods manufacturing companies, on the other hand novel technology and innovative business models are also threatening established service businesses, such as spare part supply and field service provision. In our research project BM@APPRISE we are investigating business models that are still able to monetise adequately under Industry 4.0 conditions.
Contact: Stefan Ohlig
Machine Learning is capable to predict failures using data already available from the machine’s build-in sensors and PLC. In our interdisciplinary research project ML@APPRISE we are cooperatively exploring industrial application scenarios for Machine Learning with computing specialists of the Kompetenzzentrums Netzwerke und verteilte Systeme. In addition to the exploration of (mechanical) engineering-friendly applications, we also explore service business models arising from the introduction of ML-technology.
Contact: Prof. Dr. Dirk Stegelmeyer