Abstract
A highly competitive global market and rapid technological changes have induced a transformation in the manufacturing industry. In order to stay competitive, companies are intensifying the collection of life cycle data from their products in order to add customized digital services. The resulting digitally-enabled Product-Service Systems (PSS) can boost differentiation, but concrete business opportunities and their implementation often remain vague. An example is the data-driven assessment of machine components health status. While such information could be used to generate services like predictive maintenance or remanufacturing, the necessary data and algorithms to predict the remaining useful life and ways to convey the value to the customer are often unclear. This paper illustrates the engineering of a predictive maintenance service base on operational machine data. Furthermore, possible PSS offerings and the related business models are analysed. The results are tested in a use case from the manufacturing industry and finally implications for digitally-enabled PSS are discussed.
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Acknowledgements
This research has been funded by the German Federal Ministry of Education and Research (BMBF) through the project “LongLife” (033R246A) and the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project “Mittelstand 4.0 – Kompetenzzentrum Bremen” (01MF17004B). The authors wish to acknowledge the funding agency and all project partners for their contribution.
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Wiesner, S., Egbert, L., Zitnikov, A. (2022). Using Operational Data to Represent Machine Components Health and Derive Data-Driven Services. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, vol 664. Springer, Cham. https://doi.org/10.1007/978-3-031-16411-8_35
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