Management of Virtual Models with Provenance Information in the Context of Product Lifecycle Management: Industrial Case Studies

  • Iman MorshedzadehEmail author
  • Amos H. C. Ng
  • Kaveh Amouzgar
Part of the Decision Engineering book series (DECENGIN)


Using virtual models instead of physical models can help industries reduce the time and cost of developments, despite the time consuming process of building virtual models. Therefore, reusing previously built virtual models instead of starting from scratch can eliminate a large amount of work from users. Is having a virtual model enough to reuse it in another study or task? In most cases, not. Information about the history of that model makes it clear for the users to decide if they can reuse this model or to what extent the model needs to be modified. A provenance management system (PMS) has been designed to manage provenance information, and it has been used with product lifecycle management system (PLM) and computer-aided technologies (CAx) to save and present historical information about a virtual model. This case study presents a sequence-based framework of the CAx-PLM-PMS chain and two application case studies considering the implementation of this framework.


Virtual models Provenance Product lifecycle management Virtual models CAx Discrete event simulation Meta model Cutting simulation 



This work is partially financed by the Knowledge Foundation (KKS), Sweden, through the IPSI Research School. The authors would also like to acknowledge Dr. Jan Oscarsson and Professor Manfred Jeusfeld for their considerable contributions in developing the PMS described in this paper and Marcus Barstorp for the implementation of the ManageLinks application. Additionally, Dr. Marcus Frantzén at Volvo Car Corporation, and Janne Sillanpaa of InterSystems have also given valuable inputs to the project.


  1. 1.
    Maria A (1997) Introduction to modeling and simulation. In Proceedings of the 29th conference on winter simulation [Internet]. IEEE Computer Society, Washington, DC, USA, pp 7–13. Available from:
  2. 2.
    Schumann M, Schenk M, Bluemel E (2011) Numerically controlled virtual models for commissioning, testing and training. In: Virtual reality & augmented reality in industry [Internet]. Springer, Berlin, Heidelberg, pp 163–170 [cited 2017 Oct 12]. Available from:
  3. 3.
    Banks J (2014) Discrete-event system simulationGoogle Scholar
  4. 4.
    Groover MP (2016) Groover’s principles of modern manufacturing SI version, Global Edition. SI Version, Global Edition edition. Wiley, Hoboken, New JerseyGoogle Scholar
  5. 5.
    Crnkovic I, Asklund U, Dahlqvist AP (2003) Implementing and integrating product data management and software configuration management. Artech Print on Demand, BostonzbMATHGoogle Scholar
  6. 6.
    Stark J (2005) Product lifecycle management: 21st century paradigm for product realisation [Internet]. Springer, London [cited 2016 May 10]. Available from:
  7. 7.
    Saaksvuori A, Immonen A (2005) Product lifecycle management. Springer Science & Business MediaGoogle Scholar
  8. 8.
    Martin P, D’Acunto A (2003) Design of a production system: an application of integration product-process. Int J Comput Integr Manuf 16:509–516CrossRefGoogle Scholar
  9. 9.
    Tae-hyuck Y, Gun-yeon K, Sang-do N PPR information managements for automotive die shoP 14Google Scholar
  10. 10.
    Smirnov A, Sandkuhl K, Shilov N, Kashevnik A (2013) “Product-Process-Machine” system modeling: approach and industrial case studies. In: Grabis J, Kirikova M, Zdravkovic J, Stirna J (eds) The practice of enterprise modeling [Internet]. Springer, Berlin, pp 251–265 [cited 2018 Mar 27]. Available from:
  11. 11.
    Oscarsson J, Jeusfeld MA, Jenefeldt A (2015) Towards virtual confidence—extended product lifecycle management. In: Bouras A, Eynard B, Foufou S, Thoben K-D (eds) Product lifecycle management in the era of internet of things [Internet]. Springer International Publishing, pp 708–717 [cited 2016 Apr 27]. Available from:
  12. 12.
    Ram S, Liu J (2007) Active conceptual modeling of learning. In: Chen PP, Wong LY (eds) Springer, Berlin, pp 17–29. Available from:
  13. 13.
    Inmon WH, Zachman JA, Geiger JG (1997) Data stores, data warehousing and the Zachman framework: managing enterprise knowledge, 1st edn. McGraw-Hill, Inc., New York, NY, USAGoogle Scholar
  14. 14.
    Iriondo A, Oscarsson J, Jeusfeld MA (2017) Simulation data management in a product lifecycle management context. IOS Press, pp 476–481 [cited 2017 Nov 24]. Available from:
  15. 15.
    Amouzgar K (2018) Metamodel based multi-objective optimization with finite-element applications [cited 2018 Oct 18]. Available from:

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Iman Morshedzadeh
    • 1
    Email author
  • Amos H. C. Ng
    • 1
  • Kaveh Amouzgar
    • 1
  1. 1.School of Engineering ScienceUniversity of SkövdeSkövdeSweden

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