Virtual Production Intelligence: Process Analysis in the Production Planing Phase

  • Daniel Schilberg
  • Tobias Meisen
  • Rudolf Reinhard
Conference paper


To gain a better and deeper understanding of cause and effect dependencies in complex production processes it is necessary to represent these processes for analysis as good and complete as possible. Virtual Production is a main contribution to reach this objective. To use the Virtual Production effectively in this context, a base that allows a holistic, integrated view of information that is provided by IT tools along the production process has to be created. The goal of such an analysis is the possibility to identify optimization potentials in order to increase product quality and production efficiency. The presented work will focus on a simulation based planning phase of a production process as core part of the Virtual Production. An integrative approach which represents the integration, analysis and visualization of data generated along such a simulated production process is introduced. This introduced system is called Virtual Production Intelligence and in addition to the integration possibilities it provides a context-sensitive information analysis to gain more detailed knowledge of production processes.


Analysis Digital factory Laser cutting Production technology Virtual production Virtual production intelligence VPI 


  1. 1.
    VDI Guideline 4499, Sheet 1, 2008: Digital FactoryGoogle Scholar
  2. 2.
    VDI Guideline 4499, Sheet 2, 2011: Digital FactoryGoogle Scholar
  3. 3.
    D. Schilberg, T. Meisen, R. Reinhard, Virtual production—the connection of the modules through the virtual production intelligence, in Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2013, WCECS, San Francisco, USA, 23–25 Oct, (2013), pp. 1047–1052Google Scholar
  4. 4.
    R. Lauber, P. Göhner, Prozessautomatisierung, 1. 3. Aufl. (Springer, Berlin, 1999)Google Scholar
  5. 5.
    H. Kagermann, W. Wahlster, J. Helbig, Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0—Abschlussbericht des Arbeitskreises Industrie 4.0 (Forschungsunion im Stifterverband für die Deutsche Wissenschaft, Berlin, 2012)Google Scholar
  6. 6.
    DIN EN ISO 10303Google Scholar
  7. 7.
    M. Nagl, B. Westfechtel, Modelle, Werkzeuge und Infrastrukturen zur Unterstützung von Entwicklungsprozessen. Symposium (Forschungsbericht (DFG)), 1. Aufl. (Wiley-VCH, Weinheim, 2003), S. 331–332Google Scholar
  8. 8.
    C. Horstmann, Integration und Flexibilitat der Organisation Durch Informationstechnologie, 1. Aufl. (Gabler Verlag., Wiesbaden, 2011), S. 156–162Google Scholar
  9. 9.
    Daniel Schilberg, Architektur eines Datenintegrators zur durchgängigen Kopplung von verteilten numerischen Simulationen (VDI-Verlag, Düsseldorf, 2010)Google Scholar
  10. 10.
    T. Meisen, P. Meisen, D. Schilberg, S. Jeschke, Application integration of simulation tools considering domain specific knowledge, in Proceedings of the 13th International Conference on Enterprise Information Systems (2011)Google Scholar
  11. 11.
    R. Reinhard, T. Meisen, T. Beer, D. Schilberg, S. Jeschke, A framework enabling data integration for virtual production. in Enabling Manufacturing Competitiveness and Economic Sustainability; Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011), Montreal, Canada, Hrsg. v. Hoda A. ElMaraghy, Berlin Heidelberg, 2–5 Oct (2011) S. 275–280Google Scholar
  12. 12.
    B. Byrne, J. Kling, D. McCarty, G. Sauter, P. Worcester, The Value of Applying the Canonical Modeling Pattern in SOA, IBM (The information perspective of SOA design, 4) (2008)Google Scholar
  13. 13.
    M. West, Developing High Quality Data Models, 1. Aufl. (Morgan Kaufmann, Burlington, 2011)Google Scholar
  14. 14.
    W. Yeoh, A. Koronios, Critical success factors for business intelligence systems. J. Comput. Inf. Syst. 50(3), 23 (2010)Google Scholar
  15. 15.
    ISO/IEC 2382-01Google Scholar
  16. 16.
    M. Daconta, L. Obrst, K. Smith, The Semantic Web: The Future of XML, Web Services, and Knowledge Management (Wiley, New York, 2003)Google Scholar
  17. 17.
    D. Schilberg, A. Gramatke, K. Henning, Semantic interconnection of distributed numerical simulations via SOA, in Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2008, WCECS, San Francisco, USA, 22–24 Oct (2008) pp. 894–897Google Scholar
  18. 18.
    D. Schilberg, T. Meisen, R. Reinhard, S. Jeschke, Simulation and Interoperability in The Planning Phase of Production Processes, in ASME 2011 International Mechanical Engineering Congress and Exposition, Hrsg. v. ASME, Denver (2011)Google Scholar
  19. 19.
    A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (Wiley, Chichester, 2004)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Daniel Schilberg
    • 1
  • Tobias Meisen
    • 1
  • Rudolf Reinhard
    • 1
  1. 1.Institute of Information Management in Mechanical EngineeringRWTH Aachen University GermanyAachenGermany

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