Research Group Applied Research in Industrial Service (APPRISE)
The digitalization of the industrial service business is the topic of the Applied Research in Industrial Service (APPRISE) research group. One focus is on Augmented Reality. All research activities are conducted cooperatively with Professor Rakesh Mishra from the Centre for Efficiency and Performance Engineering (CEPE) at the University of Huddersfield. Our research is funded by the Frankfurt University of Applied Sciences and by external grants from industry.
Day-long equipment downtimes and cost-intensive deployments of field service technicians all around the world: this situation is not unusual in industrial field services. Remote augmented reality in support of field services - i.e., the provision of technical services using real-time collaboration technologies and "augmented reality" - has the potential to make industrial service more efficient and sustainable. In our research project AR@APPRISE, we are investigating how this can be achieved. In industrial case studies, we have investigated real-world use cases as well as barriers and key activities for the adoption of the technology. The results from our collaboration with 25 industrial companies are available here in our white paper. Not least due to the Covid 19 pandemic, more and more companies are using remote augmented reality. As a result, the database on which we investigate the success factors of remote augmented reality implementation is constantly growing.
Contact: Maike Müller
Digital transformation and Industry 4.0 are shaking up industrial service. New technologies such as Augmented Reality or new forms of organization such as industry platforms are emerging and promise many advantages – but at the same time also threaten the classic, highly profitable business areas of spare parts and field service of machine and plant manufacturers. In our research project BM@APPRISE, we are investigating business models through which the services of machine and plant manufacturers can achieve the financial value that corresponds to their high quality, even in a digitalized industry.
Contact: Stefan Ohlig
When does a plant need maintenance? This question can be answered by using existing sensor and control data from machines, thanks to machine learning technology – the keyword being predictive maintenance. In our interdisciplinary research project ML@APPRISE, computer scientists from the Center of Competence for networks and distributed systems (CECNDS) are investigating this topic together with experts from mechanical engineering. On the basis of concrete industrial problems, not only application systems are being researched, but also the resulting business models.
Contact: Anna Binder
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- Slide 4 Cross Innovation Network Meeting, IHK Offenbach, October 2019
- Slide 5 September 2019, Congress: APPRISE presents Research Results in Huddersfield
- Slide 6 Brown Bag Seminar, November 2019
- Slide 7 APPRISE at RoBoTec PTC GmbH, May 2020
- Slide 8 APPRISE at the 1st international Conference on eXtended Reality, July 2022
- Slide 9 APPRISE at the 8th VDMA Future Business Summit, Buschhütten, September 2022
ARemoS – Augmented Reality-based Remote Service Business Models
Services in the service business of mechanical and plant engineering that are provided via remote access through Augmented Reality can be efficient, sustainable and cost-saving – and against the background of pandemic-related travel restrictions, they are more important than ever. In our research project ARemoS, we identify design options for AR-based remote service business models and translate them into a taxonomy, i.e., an empirical classification system. In addition, we are developing a typology of AR-based remote service business models based on interviews with employees responsible for service at as many machine and plant manufacturers as possible. The results can be used in practice as a tool for the future design of such business models. Furthermore, our work lays the foundation to put future research in this area on a systematic basis. The research project is funded by the promotion scheme "Research for Practice 2021" with 40,000 EUR.
Contact: Stefan Ohlig
In our research project RoBoCut-AR, we are developing an AR-based remote service concept for fully autonomous ornamental plant and crop production with "RoBoCut" together with the Bremen-based technology start-up RoBoTec PTC. RoBoCut experts can support local operators of the RoBoCut from afar through real-time collaboration using video streams and integrated AR capabilities. This can reduce unnecessary travel by service technicians, ensure short downtimes and guarantee high productivity.
The project is being funded by the German Federal Ministry for Economic Affairs and Energy as part of the Innovation Program for Business Models and Pioneering Solutions (IGP) with 171,300 euros over a period of 24 months from October 1, 2020 to September 30, 2022 (funding code: 16GP100102).
Contact: Stefan Ohlig
- Aktueller Stand von Wissenschaft und Technik zu Condition Monitoring (Systatmic Literature Review)
- Analyse von Servicestrategien im chinesischen Markt
- Applying consumer products in industrial applications
- AR@APPRISE Geschäftsmodell-Evaluationstools/-methoden (Systematic Literature Review)
- AR@APPRISE Metanalyse Einflussfaktoren Implementierungsprozess von innovativen Technologien
- Ausbau des Dienstleistungsgeschäfts eines Sägenherstellers mittels e-Shop
- Auswertung von Maschinendaten zur Weiterentwicklung der Produkte-Autinity
- CRM@APPRISE Evalutation eines CRM-Systems fur die Forschung bei APPRISE
- Flexibility in Production Systems – Systematic Literature Review
- Machbarkeitsstudie 3D Druck Ersatzteile
- Machbarkeitsstudie Geschäftsfeld gebrauchter Medizinprodukte
- Platform-driven business in manufacturing (Systematic Literature Review) follow up
- Predictive Maintenance – Application in Industry
- Servitization, Digitalization and Risk Mitigation (Systematic Literature Review)
- Stand Lehrwerke in Service Engineering und Service Management
- Teaching@APPRISE Getriebemontageübung in Augmented Reality Umgebung überführen
- VDMA Betriebswirtschaftliche Blätter – Service
- Vergleich Machine Learning Software für Predictive Maintenance aus Sicht des Anwenders
- Weiterentwicklung einer prototypischen Umformmaschine
If you are interested in working on one of these topics, please contact Herrn Prof. Dr. Dirk Stegelmeyer.
- Die Anwendung des maschinellen Lernens zur Predictive Maintenance an Beschichtungsanlagen
- AR@APPRISE: Entwicklung eines Klassifizierungsmodells für Anwendungsfälle und anwendende Unternehmen von Augmented Reality im After-Sales Service des Maschinen- und Anlagenbaus
- Datengetriebene Geschäftsmodellentwicklung am Beispiel Beschichtungsanlagen
- AR@APPRISE: Identifizierung und Klassifizierung von Augmented Reality (AR)-basierten Dienstleistungsangeboten im deutschen Maschinen- und Anlagenbau)
- AR@APPRISE: Identifizierung und Klassifizierung von Augmented Reality (AR)-basierter Fernwartungssoftware für den industriellen Einsatz)
- AR@APPRISE: The industrial service platform – Reviewing opportunities, challenges and applications
- AR@APPRISE: Barriers and Benefits of Mobile Collaborative Augmented Reality and Remote Monitoring Technology for Industrial Service Delivery
- AR@APPRISE: Unterscheidungsmerkmale von Augmented Reality basierten Remote Support Softwares
- Service Apps zu Erfassung von Servciefällen durch Kunden: Vergleich von Einführungsstrategien, Probleme und Erfahrungen bei Industrieunternehmen
Visit us on ORCID and ResearchGate:
Prof. Dr. Dirk Stegelmeyer:
Ms. Maike Müller:
Mr. Stefan Ohlig:
Mr. David Breitkreuz