Product Lifecycle Management Challenges of CPPS

  • Detlef GerhardEmail author


In the chapter Product Lifecycle Management (PLM) Challenges of CPPS, data and information management issues arising from the advanced use of modern product development and engineering methods are addressed. These advanced methods are required for engineering processes of smart systems and individualized products with high complexity and variability. Emphasis is put on challenges of the life-cycle oriented information integration of products and the respective Cyber-Physical Production Systems (CPPS). Furthermore, the chapter addresses data and information management problems coming from integration of the use and operation phase of products and systems in terms of forward and backward information flows.


Product lifecycle management (PLM) Cyber-physical production systems (CPPS) Information management Model based systems engineering (MBSE) Digital Twin 


  1. Ashton, K.: That ‘internet of things’ thing. RFiD J. 22, 97–114. (2009). Retrieved 25 Jul 2016
  2. Ben Khedher, A., Henry, S., Bouras, A.: Integration between MES and product lifecycle management. In IEEE International Conference on Emerging Technologies and Factory Automation (ETFA’11), pp. 8–13. Toulouse, 2011Google Scholar
  3. Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horvath, L., Bernard, A., Harik, R.F., Gao, W.: The evolution, challenges, and future of knowledge representation in product design systems. Comput. Aided Des. 2013, 204–228 (2013)Google Scholar
  4. Charaf, K., Ding, H.: Is overall equipment effectiveness (OEE) universally applicable? The case of Saint-Gobain. Int. J. Econ. Fin. 7(2), 241–252 (2015)Google Scholar
  5. Cheng, B.H.C., Atlee, J.M.: Research directions in requirements engineering. In: Proceedings of Future of Software Engineering, pp. 285–303. IEEE Computer Society, Washington (2007)Google Scholar
  6. Gerhard, D.: The role of semantic technologies in future PLM. In: Fathi, M. (ed.) Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives, pp. 157–170. Springer, Berlin (2012)Google Scholar
  7. Gerhard, D., Lutz, C.: Rechnerunterstütztes Konfigurieren und Auslegen kundenindividueller Produkte. ZWF Z. wirtschaftlichen Fabrikbetrieb. 3, 103–104 (2011)Google Scholar
  8. Gerhard, D., Weilguny, L.: Applied feature technology – review of developing a generic solution facilitating data-consistency and enabling knowledge-based engineering. In: CIRP Design Conference 2008, Twente, NL, 2008Google Scholar
  9. Gill, H.: Cyber-physical systems – beyond ES, SNs, and SCADA. In: Presentation in the Trusted Computing in Embedded Systems (TCES) Workshop. (2010). Retrieved 25 Jul 2016
  10. Gröger, C., Mitschang, B., Niedermann, F.: Data mining-driven manufacturing process optimisation. In: Proceedings of the World Congress on Engineering, vol. III, pp. 1–7, 2012Google Scholar
  11. Gunes, V., et al.: A survey on concepts, applications, and challenges in cyber-physical systems. KSII Trans. Internet Inf. Syst. 8(12), (2014)Google Scholar
  12. IEC 62264-3: Enterprise-control system integration – Part 3: activity models of manufacturing operations management. Beuth, Berlin (2014)Google Scholar
  13. INCOSE: International council on systems engineering: systems engineering vision 2020 INCOSE-TP-2004-004-02 ver. 2.03. (2007). Retrieved 25 Jul 2016
  14. ISO 10303: Industrial Automation Systems and Integration – Product Data Representation and Exchange. International Organization for Standardization (ISO), Geneva (1994)Google Scholar
  15. ISO 14306: Industrial Automation Systems and Integration – JT File Format Specification for 3D Visualization. International Organization for Standardization (ISO), Geneva (2012)Google Scholar
  16. Laney, D.: 3-D data management: controlling data volume, velocity and variety. (2001). Retrieved 25 Jul 2016
  17. Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Elsevier Manuf. Lett. 3, 18–23 (2015)CrossRefGoogle Scholar
  18. Matthias, K., Kane, S.P.: Docker Up & Running, Shipping Reliable Containers in Production. O’Reilly Media, Sebastopol, CA (2015)Google Scholar
  19. Monostori, L.: Cyber -physical production systems: roots, expectations and R&D challenges. Variety management in manufacturing. Proceedings of the 47th CIRP Conference on Manufacturing Systems. Proc. CIRP. 17(2014), 9–13 (2014)CrossRefGoogle Scholar
  20. Mouat, A.: Using Docker, Developing and Deploying Software with Containers. O’Reilly Media, Sebastopol, CA (2015)Google Scholar
  21. Nakajima, S.: TPM tenkai, JIPM Tokyo. (1982). Retrieved 25 Jul 2016
  22. Nattermann, R., Anderl, R.: Approach for a data-management-system and a proceeding-model for the development of adaptronic systems. In: Proceedings for the ASME International Mechanical Engineering Congress & Exposition (IMECE), Vancouver, 2010Google Scholar
  23. Newman, S.: Building Microcervices, Designing Fine-Grained Systems, p. 18. O’Reilly Media, Sebastopol, CA (2015)Google Scholar
  24. OSLC4MBSE: Working Group. (2013). Retrieved 25 Jul 2016
  25. Ostad-Ahmad-Ghorabi, H., Rahmani, T., Gerhard, D.: Forecasting environmental profiles in the early stages of product development by using an ontological approach. In: Abramovici, M., Stark, R. (eds.) Smart Product Engineering, pp. 715–724. Springer, Berlin/Heidelberg (2013)Google Scholar
  26. PROSTEP: White Paper Datenmanagement für Smart Systems Engineering (Smart SE). ProSTEP iViP (2015)Google Scholar
  27. Quintana, V., Rivest, L., Pellerin, R., Venne, F., Kheddouci, F.: Will model-based definition replace engineering drawings throughout the product lifecycle? A global perspective from aerospace industry. Comput. Ind. 61(5), 497–508 (2010)CrossRefGoogle Scholar
  28. Schuh, G.: Produktionsplanung und -steuerung Grundlagen, Gestaltung und Konzepte, 3rd edn. Springer, Berlin (2006)CrossRefGoogle Scholar
  29. Seibold, Z., Furmanns, K.: Dezentrale Koordinationsmechanismen für Multifunktionalität und Wiederverwendbarkeit. In: Bauernhansl, et al. (eds.) Industrie 4.0 in Produktion, Automatisierung und Logistik, pp. 1–17. Springer, Berlin (2015)Google Scholar
  30. Sethi, A.K., Sethi, S.P.: Flexibility in manufacturing: a survey. Int. J. Flex. Manuf. Syst. 2, 289–328 (1990)CrossRefGoogle Scholar
  31. SYSML: The SysML specification, v 1.0. (2007). Retrieved 25 Jul 2016
  32. Tolio, T., Ceglarek, D., El Maraghy, H.A., Fischer, A., Hu, S.J., Laperrière, L., Newman, S.T., Váncza, J.: SPECIES – co-evolution of products, processes and production systems. CIRP Ann. Manuf. Technol. 59(2), 672–693 (2010)CrossRefGoogle Scholar
  33. Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerospace Eng. 2011, Article ID 154798 (2011).
  34. VDI 2206: VDI-Guideline 2206 – design methodology for mechatronic systems. Beuth, Berlin (2004)Google Scholar
  35. VDI 2219: VDI-Guideline 2219 – information technology in product development – introduction and usage of PDM systems. Beuth, Berlin (2002)Google Scholar
  36. VDI 2221: VDI-Guideline 2221 – systematic approach to the development and design of technical systems and products. Beuth, Berlin (1993)Google Scholar
  37. VDI 5600: VDI-Guideline 5600 – manufacturing execution systems (MES). Beuth, Berlin (2007)Google Scholar
  38. Welp, E.G., Labenda, P., Bludau, C.: Usage of ontologies and software agents for knowledge-bases design of mechatronic systems. In: Proceedings of the 16th International Conference on Engineering Design (ICED 07), Paris, 2007Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Mechanical Engineering Informatics and Virtual Product Development (MIVP) Research GroupTechnische Universität WienWienAustria

Personalised recommendations