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.
Extended equipment downtimes and costly deployments of field service technicians are common challenges in industrial field service operations worldwide. The adoption of Remote Augmented Reality, which involves delivering remote service via collaboration technology enriched with Augmented Reality, holds the promise of enhancing the efficiency and sustainability of industrial service operations. Our research project, AR@APPRISE, aims to explore the potential of this approach. Through industrial case studies, we have examined real-world use cases and identified barriers and key activities for the adoption of this technology. The findings from our collaboration with 25 industrial companies are detailed in our 2021 white paper Augmented Reality Remote Service. Influenced particularly by the Covid-19 pandemic, an increasing number of companies are embracing remote augmented reality. Consequently, the database used to analyze the success factors of remote augmented reality implementation is continuously expanding. Our most current study investigates 130 capital equipment companies, and the management summary of this study is available in our 2024 white paper Einführung von Remote Augmented Reality im Maschinen- und Anlagenbau (available in German only).
Contact: Maike Müller
The landscape of industrial service is being reshaped by digital transformation and Industry 4.0. Emerging technologies such as Augmented Reality, as well as new organizational models such as industry platforms, offer numerous advantages, yet simultaneously pose a threat to the traditional, highly lucrative business segments of spare parts and field service for machine and plant manufacturers. In our research project, BM@APPRISE, we are exploring innovative business models that enable machine and plant manufacturers to realize the financial value commensurate with their high-quality services, even in the context of a digitalized industry.
Contact: Stefan Ohlig
The question of when a plant requires maintenance can be addressed by leveraging existing sensor and control data from machines, facilitated by machine learning technology – a concept known as Predictive Maintenance. In our interdisciplinary research project, ML@APPRISE, computer scientists from the Center of Competence for networks and distributed systems (CECNDS) are collaborating with mechanical engineering experts to investigate this topic. This research delves into not only the development of application systems based on real industrial challenges but also the exploration of resultant business models.
Contact: Anna Binder
- Slide 1
- Slide 2 APPRISE at RoBoTec PTC GmbH, May 2020
- Slide 3 APPRISE at the 1st international Conference on eXtended Reality, July 2022
- Slide 4 APPRISE at the 8th VDMA Future Business Summit, Buschhütten, September 2022
- Slide 5 INDAS at 10th Spanish-German Symposium on Applied Computer Science (SGSOACS 2024)
APPRISE at RoBoTec PTC GmbH, May 2020
APPRISE at the 1st international Conference on eXtended Reality, July 2022
APPRISE at the 8th VDMA Future Business Summit, Buschhütten, September 2022
INDAS at 10th Spanish-German Symposium on Applied Computer Science (SGSOACS 2024)
Publications
- Müller M, Ohlig S, Stegelmeyer D & Mishra R (2024)„Comparing Adopter, Tester, and Non-adopter of Collaborative Augmented Reality for Industrial Services“. In Lecture Notes in Networks and Systems (LNNS) (in print). Springer (ISSN: 2367-3370).
- Ohlig, S., Breitkreuz, D., Aliyu, A., Mishra, R., Stegelmeyer, D. (2024) „Towards a taxonomy of design options for augmented reality-based remote service business models.“ Engineering Management in Production and Services, 16(2), 128–147. https://doi.org/10.2478/emj-2024-0018
- Müller M, Ohlig S, Stegelmeyer D (2024) „Remote Augmented Reality im Maschinen- und Anlagenbau einführen: Management Summary einer Studie mit 130 Industrieunternehmen.“ WHITE PAPER, Frankfurt University of Applied Science, Frankfurt am Main. https://doi.org/10.48718/xp3h-4j47
- Ohlig, S., Müller, M., & Stegelmeyer, D. (2024) „Augmented Reality Remote Service Business Models in the Mechanical and Plant Engineering Industry. Results of an Interview Study with 36 Manufacturers“ (WHITE PAPER 3). Frankfurt am Main. Frankfurt University of Applied Sciences. https://doi.org/10.48718/67fr-ay51
- Binder de Serdio, A M, Stegelmeyer, D, Butt, F S (2024). „Early Indicators of Project Abandonment in Industry-Academia Collaborations: Developing an Assessment Framework for Industrial Data Science Projects.” In Proceedings of the 10th Spanish-German Symposium on Applied Computer Science (SGSOACS 2024), Cádiz, Spain. https://zenodo.org/doi/10.5281/zenodo.11916306
- Ohlig, S., Breitkreuz, D., Mishra, R. & Stegelmeyer, D., 2023. „Business Model Research on Industrial Augmented Reality: A Systematic Literature Review on the Current State and Future Research Areas” In L.-C. Tang (Ed.), Advances in Transdisciplinary Engineering. Industrial Engineering and Applications (pp. 489–499). IOS Press. https://doi.org/10.3233/ATDE230074
- Butt, F. S., Schäfer, J., Wagner M. F., Stegelmeyer, D. & Gómez-Ullarte Oteiza, D., 2023.„Application of CRISP-DM and DMME to a Case Study of Condition Monitoring of Lens Coating Machines” In 2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), Brescia, Italy, 2023, pp. 409-414 https://doi.org/10.1109/MetroInd4.0IoT57462.2023.10180178
- Stegelmeyer, D., 2023. „Gastbeitrag: Serviceprodukte entwickeln“ In VDMA e.V. (Ed.), VDMA Magazin (Vol. 102, p. 16). VDMA Verlag.
- Müller, M., Stegelmeyer, D. and Mishra, R., 2023. „Development of an augmented reality remote maintenance adoption model through qualitative analysis of success factors” Operations Management Research. https://doi.org/10.1007/s12063-023-00356-1
- Möwis, G., Stegelmeyer, D., Struyf, B., & Matthyssens, P., 2022. "Leveraging ecosystem partnerships for Industry 4.0-enabled value creation: A Delphi-study." In V. Kirov & B. Malamin (Chairs), Inclusive Futures for Europe, addressing the digitalisation challenges: BEYOND4.0 Scientific Conference Sofia 2021 Proceedings, Sofia.
- Breitkreuz, D., Müller, M., Stegelmeyer, D., & Mishra, R., 2022. „Augmented Reality Remote Maintenance in Industry: A Systematic Literature Review”. In L. T. de Paolis, P. Arpaia, & M. Sacco (Eds.), Lecture Notes in Computer Science (LNCS) (Vol. 13446, pp. 287–304). Springer. https://doi.org/10.1007/978-3-031-15553-6
- Maiser, E., Küster, A., Thomin, P., Moller, B., Kirstgen, A., Lerch, C., & Stegelmeyer, D. 2022. „ Future Services 2035: Zukunftsbilder für den Maschinen- und Anlagenbau“ (VDMA Future Business, Band 8). Frankfurt am Main.
- Müller, M., Stegelmeyer, D., & Mishra, R., 2020. "Introducing a Field Service Platform". In: A. Ball, L. Gelman, & B. K. N. Rao (Eds.), Smart Innovation, Systems and Technologies. Advances in Asset Management and Condition Monitoring (Vol. 166, pp. 195–205). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-57745-2_17
- Ohlig, S., Stegelmeyer, D., Mishra, R., & Müller, M., 2020. "Exploring the Impacts of Using Mobile Collaborative Augmented Reality on the Field Service Business Model of Capital Goods Manufacturing Companies". In: A. Ball, L. Gelman, & B. K. N. Rao (Eds.), Smart Innovation, Systems and Technologies. Advances in Asset Management and Condition Monitoring (Vol. 166, pp. 473–484). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-57745-2_40
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
The seminar AR-based Remote Service provides basic knowledge on the implementation of augmented reality in the service business of mechanical and plant engineering. The aim of the seminar is to prepare decision-makers in industrial companies for an AR implementation project in service.
Further information on the seminar: fra-uas.de/AR
- 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
Contact
Scientific staff:
- Gabriel Möwis B.Eng. Service Engineering - graduate, M.Sc. Innovation & Entrepreneurship
- Maike Müller B.Eng. Service Engineering - graduate, PhD Engineering (University of Huddersfield)
Student assistant:
- Christian Kapfenberger B.Eng. Service Engineering - graduate
- Jakob Ruf B.Eng. Service Engineering - graduate
- Lukas Weyer B.Eng. Service Engineering - graduate
- Merve Yabanli B.Eng. Service Engineering - student
- Timo Laukhardt B.Eng. Service Engineering - student