Towards a Hybrid Process Model Approach in Production Systems Engineering

  • Dietmar WinklerEmail author
  • Lukas Kathrein
  • Kristof Meixner
  • Peter Staufer
  • Michael Pauditz
  • Stefan Biffl
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1060)


Context. In Production Systems Engineering (PSE), experts of different domains collaborate in loosely coupled engineering processes to collectively plan and develop an Automation System (AS). Due to limited collaboration capabilities of discipline-specific tools, engineering knowledge is often lost and needs to be recovered manually with considerable effort. Information backflow is often limited due to incompatible artifacts and engineering models. Goal. Main goal is to establish a hybrid process combined with an improved data model to overcome initial limitations of an existing engineering process. Method. In a case study at a large-scale automation engineering organization, we investigate challenges and requirements for a hybrid Engineering Process in PSE. For efficient knowledge exchange, we build on a Product, Process, and Resource (PPR) concept that aims at bridging the gap between engineering disciplines, project phases, and artifacts. Results. The proposed hybrid process improved knowledge exchange and backflows and allows experts to maintain planning processes within their scope. Conclusions. Although the PPR concept was found useful, initial effort is needed for analyzing processes and data for PPR concept implementation.


Production Systems Engineering PPR Case study Engineering process improvement Hybrid engineering process 


  1. 1.
    Biffl, S., Gerhard, D., Lüder, A.: Introduction to the multi-disciplinary engineering for cyber-physical production systems. In: Biffl, S., Lüder, A., Gerhard, D. (eds.) Multi-Disciplinary Engineering for Cyber-Physical Production Systems, pp. 1–24. Springer, Cham (2017). Scholar
  2. 2.
    Tröls, M.A., Mashkoor, A., Egyed, A.: Live and global consistency checking in a collaborative engineering environment. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1776–1785. ACM (2019)Google Scholar
  3. 3.
    Schleipen, M., Lüder, A., Sauer, O., Flatt, H., Jasperneite, J.: Requirements and concept for plug-and-work. at-Automatisierungstechnik 63(10), 801–820 (2015)CrossRefGoogle Scholar
  4. 4.
    Wieringa, R.: Design Science Methodology for Information Systems and Software Engineering. Springer, Berlin (2014). Scholar
  5. 5.
    Wang, Y., King, G.: Software Engineering Processes: Principles and Applications. CRC Press, Boca Raton (2000)Google Scholar
  6. 6.
    Schwaber, K., Beedle, M.: Agile Software Development with Scrum, vol. 1. Prentice Hall, Upper Saddle River (2002)zbMATHGoogle Scholar
  7. 7.
    Kuhrmann, M., et al.: Hybrid software and system development in practice: waterfall, scrum, and beyond. In: Proceedings of the 2017 International Conference on Software and System Process, pp. 30–39. ACM (2017)Google Scholar
  8. 8.
    Rausch, A., Bartelt, C., Ternite, T., Kuhrmann, M.: The V-modell XT applied-model-driven and document-centric development. In: 3rd World Congress for Software Quality, vol. 3, pp. 131–138 (2005)Google Scholar
  9. 9.
    Lüder, A., Föhr, M., Hundt, L., Hoffmann, M., Langer, Y., Frank, S.: Aggregation of engineering processes regarding the mechatronic approach. In Proceedings of ETFA (2011)Google Scholar
  10. 10.
    Paetzold, K.: Product and systems engineering/CA* tool chains. In: Biffl, S., Lüder, A., Gerhard, D. (eds.) Multi-Disciplinary Engineering for Cyber-Physical Production Systems, pp. 27–62. Springer, Cham (2017). Scholar
  11. 11.
    Wiesner, S., Thoben, K.-D.: Cyber-physical product-service systems. In: Biffl, S., Lüder, A., Gerhard, D. (eds.) Multi-Disciplinary Engineering for Cyber-Physical Production Systems, pp. 63–88. Springer, Cham (2017). Scholar
  12. 12.
    Hundt, L., Lüder, A.: Development of a method for the implementation of interoperable tool chains applying mechatronical thinking – use case engineering of logic control. In: Proceedings IEEE 17th International Conference Emerging Technologies Factory Automation (ETFA 2012), pp. 1–8, September 2012Google Scholar
  13. 13.
    Stark, J.: Product lifecycle management. Product Lifecycle Management. DE, pp. 1–29. Springer, Cham (2015). Scholar
  14. 14.
    ElMaraghy, H.A.: Changing and evolving products and systems-models and enablers. In: ElMaraghy, H. (ed.) Changeable and Reconfigurable Manufacturing Systems. SSAM, pp. 25–45. Springer, London (2009). Scholar
  15. 15.
    Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Softw. Eng. 14(2), 131 (2008)CrossRefGoogle Scholar
  16. 16.
    Allweyer, T.: BPMN 2.0: introduction to the standard for business process modeling. BoD-Books on Demand (2016)Google Scholar
  17. 17.
    Presley, A., Liles, D.H.: The use of IDEF0 for the design and specification of methodologies. In: Proceedings of the 4th Industrial Engineering Research Conference (1995)Google Scholar
  18. 18.
    Fowler, M., Kobryn, C., Scott, K.: UML Distilled: A Brief Guide to the Standard Object Modeling Language. Addison-Wesley Professional, Boston (2004)Google Scholar
  19. 19.
    Scheer, A.-W.: ARIS - vom geschaeftsprozess zum anwendungssystem, dritte, voellig neubearbeitete und erweiterte auflage (1998)Google Scholar
  20. 20.
    Klünder, J., et al.: HELENA study: reasons for combining agile and traditional software development approaches in German companies. In: Felderer, M., Méndez Fernández, D., Turhan, B., Kalinowski, M., Sarro, F., Winkler, D. (eds.) PROFES 2017. LNCS, vol. 10611, pp. 428–434. Springer, Cham (2017). Scholar
  21. 21.
    VDI Richtlinie 3695: Engineering von Anlagen—Evaluieren undoptimieren des Engineerings (2009)Google Scholar
  22. 22.
    Kathrein, L., Lüder, A., Meixner, K., Winkler, D., Biffl, S.: Process analysis for communicating systems engineering workgroups. Technical report CDL-SQI 2018–11, CDL-SQI, TU Wien, November 2018Google Scholar
  23. 23.
    Zhu, L., Bass, L., Champlin-Scharff, G.: Devops and its practices. IEEE Softw. 33(3), 32–34 (2016)CrossRefGoogle Scholar
  24. 24.
    Jäger, T., Fay, A., Lowen, U., Wagner, T.: Mining technical dependencies throughout engineering process knowledge (2011)Google Scholar
  25. 25.
    Rilling, J., Witte, R., Schuegerl, P., Charland, P.: Beyond information silos–an omnipresent approach to software evolution. Int. J. Semant. Comput. 2(04), 431–468 (2008)CrossRefGoogle Scholar
  26. 26.
    Kossak, F., et al.: Hagenberg Business Process Modelling Method. Springer, Cham (2016). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Christian Doppler Laboratory for “Security and Quality Improvement in the Production System Lifecycle”, Institute of Information Systems EngineeringTU WienViennaAustria
  2. 2.Institute of Information Systems EngineeringTU WienViennaAustria
  3. 3.Stiwa Automation GmbHAttnang-PuchheimAustria

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