Abstract
The digital transformation based on internet technologies comprises huge potentials but also challenges for the production industry. Even though some design characteristics are generally accepted for the digitized integration of machines, applications and surrounding components the inherent complexity and variety of interaction protocols, data formats and interdependencies of existing deployments in so called brownfield environments hampers the data-driven manufacturing of the future.
We propose an iterative approach where existing context data is used to encapsulate the specific complexity of each resource in order to create a flexible integration layer. Nearly all relevant resources are modeled as self-descriptive cyber-physical systems or Virtual Representations according to the setting of the physical production environment, therefore drastically reducing the required access barriers. We present a reference implementation and discuss its business implications by the example of industrial maintenance.
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References
Ahmad, R., Kamaruddin, S.: An overview of time-based and condition-based maintenance in industrial application. Comput. Ind. Eng. 63(1), 135–149 (2012)
Bader, S.R., Maleshkova, M.: Virtual representations for an iterative IoT deployment. In: Companion of The Web Conference 2018, pp. 1887–1892. International World Wide Web Conferences Steering Committee (2018)
Baines, T.S., et al.: State-of-the-art in product-service systems. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 221(10), 1543–1552 (2007)
Baines, T., Lightfoot, H., Benedettini, O., Kay, J.: The servitization of manufacturing. J. Manuf. Technol. Manag. 20(5), 547–567 (2009)
Bauer, H., et al.: Industry 4.0 after the initial hype-where manufacturers are finding value and how they can best capture it. McKinsey Digital (2016)
Bedenbender, H., et al.: Industrie 4.0 Plug-and-Produce for Adaptable Factories: Example Use Case Definition, Models, and Implementation (2017). http://www.plattform-i40.de/I40/Redaktion/EN/Downloads/Publikation/Industrie-40-%20Plug-and-Produce
Campos, J.: Development in the application of ICT in condition monitoring and maintenance. Comput. Ind. 60(1), 1–20 (2009)
Davis, J., Edgar, T., Porter, J., Bernaden, J., Sarli, M.: Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 47, 145–156 (2012)
Gitzel, R., Schmitz, B., Fromm, H., Isaksson, A., Setzer, T.: Industrial services as a research discipline. Enterp. Model. Inf. Syst. Archit. 11(4), 1–22 (2016)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Hedengren, J.D., Eaton, A.N.: Overview of estimation methods for industrial dynamic systems. Optim. Eng. 18(1), 155–178 (2017)
Jardine, A.K., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Sig. Process. 20(7), 1483–1510 (2006)
Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)
Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., Liao, H.: Intelligent prognostics tools and e-maintenance. Comput. Ind. 57(6), 476–489 (2006)
Lin, S.W., et al.: The Industrial Internet of Things Volume G1: Reference Architecture (2017). http://www.iiconsortium.org/IIRA.htm
Meier, H., Roy, R., Seliger, G.: Industrial product-service systems-IPS2. CIRP Ann. - Manuf. Technol. 59(2), 607–627 (2010)
Otto, B., Lohmann, S.: Reference architecture model for the industrial data space. Technical report, Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V (2017)
Paz, N.M., Leigh, W.: Maintenance scheduling: issues, results and research needs. Int. J. Oper. Prod. Manag. 14(8), 47–69 (1994)
Rauen, H., Mosch, C., Niggemann, O., Jasperneite, J.: Industrie 4.0 kommunikation mit OPC UA. Leitfaden zur EinfĂ¼hrung in den Mittelstand. Hg. v. VDMA und Fraunhofer-Anwendungszentrum Industrial Automation. Frankfurt am Main (2017). ISBN 978-3-8163-0709-9
Stein, B., Morrison, A.: The enterprise data lake: better integration and deeper analytics. PwC Technol. Forecast: Rethink. Integr. 1, 1–9 (2014)
Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 40, 536–541 (2016)
Swanson, L.: Linking maintenance strategies to performance. Int. J. Prod. Econ. 70(3), 237–244 (2001)
Tanuska, P., Spendla, L., Kebisek, M.: Data integration for incidents analysis in manufacturing infrastructure. In: Computing Conference, 2017, pp. 340–345. IEEE (2017)
Vargo, S.L., Lusch, R.F.: Service-dominant logic: continuing the evolution. J. Acad. Market. Sci. 36(1), 1–10 (2008)
Wolff, C., Vössing, M., Schmitz, B., Fromm, H.: Towards a technician marketplace using capacity-based pricing. In: Proceedings of the 51th Hawaii International Conference on System Sciences, pp. 1553–1562. Waikoloa (2018)
Acknowledgement
The research and development project that forms the basis for this report is funded under project No. 01MD16015 (STEP) within the scope of the Smart Services World technology program.
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Bader, S.R., Wolff, C., Vössing, M., Schmidt, JP. (2018). Towards Enabling Cyber-Physical Systems in Brownfield Environments. In: Satzger, G., PatrĂcio, L., Zaki, M., KĂ¼hl, N., Hottum, P. (eds) Exploring Service Science. IESS 2018. Lecture Notes in Business Information Processing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-030-00713-3_13
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