Typology and Implications of Equipment-as-a-Service Business Models in the Manufacturing Industry
- In the manufacturing industry, technological developments around cyber-physical systems create completely new possibilities for generating value for customers. An essential part of these developments are Equipment-as-a-Service (EaaS) business models, which promise growth with existing customers even in saturated markets and in which the interests of manufacturers and providers are aligned. However, different types of EaaS business models must be differentiated in manufacturing industry, especially with regard to the risk transfer from the customer to the provider. In this paper, a typology for different EaaS types is developed based on grounded research. Furthermore, practical implications are derived that should help with the necessary EaaS transformation, that has been previously outlined by the EaaS typology.
| Author: | Lennard Holst, Volker StichORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-031-15602-1_26 |
| ISBN: | 978-3-031-15601-4 |
| ISBN: | 978-3-031-15602-1 |
| ISSN: | 2194-0525 |
| Parent Title (English): | Smart, Sustainable Manufacturing in an Ever-Changing World: Proceedings of International Conference on Competitive Manufacturing (COMA ’22) |
| Publisher: | Springer |
| Place of publication: | Cham [u.a.] |
| Editor: | Konrad von Leipzig, Natasha Sacks, Michelle Mc Clelland |
| Document Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2023/03/04 |
| Date of first Publication: | 2023/03/04 |
| Release Date: | 2025/08/19 |
| Tag: | 03 Equipment-as-a-Service; Industrial subscription; Manufacturing industry; Typology |
| First Page: | 347 |
| Last Page: | 355 |
| Note: | Acknowledgements: The research and development project Future Data Assets that forms the basis for this report is funded within the scope of the ‘Smart Data Economy’ technology program run by the German Federal Ministry for Economic Affairs and Climate (BMWK) and is managed by the DLR project management agency. The authors are responsible for the content of this publication.
Future Data Assets:
Intelligent data accounting for the determination of the entrepreneurial data value
The objective of the research project "Future Data Assets" is to provide monetary valuation of the corporate data. For this purpose, the development and instantiation of a so-called "data balance" is sought. The data balance should serve the reporting of the entrepreneurial ability of the data management and thus close a gap with respect to the classical reporting, in which data are hardly considered or systematically evaluated.
Benefits for the target group:
The data balance as a reporting instrument should have two central components or properties:
Data balance management report (past-oriented character): Within the data balance, the data stock of a company is reported for a certain period in the past. This occurs on a key date, so that changes in data management become visible over time. Against the background of reliability, this part of the data balance must be comprehensible and verifiable by external bodies (for example, auditors).
Data balance forecast report (future-oriented character): Furthermore, the data balance should serve as a forecasting tool and thus provide information about the potential future data management of a company. For this purpose, the application of machine learning methods is examined, which also allow insights into their functioning. This part of the data balance offers the possibility to estimate future developments and to visualize the effects of planned (data-intensive) investment projects in the digital transformation
Project partners:
atlan-tec Systems GmbH, Mönchengladbach
DMG MORI Global Services GmbH, Bielefeld
Universität des Saarlandes, Saarbrücken
Associated partners:
Kuraray Europe GmbH, Hattersheim am Main
Swisdata GmbH, Vienna, Austria |
| Institute / Department: | FIR e. V. an der RWTH Aachen |
| Dienstleistungsmanagement | |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften |
