Journal of Intelligent Manufacturing

, Volume 19, Issue 5, pp 521–535 | Cite as

Configuration for mass customization: how to extend product configuration towards requirements and process configuration

  • Michel Aldanondo
  • Elise Vareilles


In order to develop mass customization, many companies use configuration software to customize their products. Although many studies already exist about Product Configuration, Requirements and Process Configuration have not been studied in detail. As all these three aspects must be considered for mass customization, the aim of this paper is to show how Product Configuration, when considered as a constraint satisfaction problem, can be extended upstream towards Requirements Configuration and downstream towards Process Configuration. Product Configuration basics are first reviewed thanks to a constraint based approach, and an analysis of industrial configuration situations is done in order to clarify mass customization needs in terms of configuration. Then upstream Requirements Configuration and downstream Process Configuration are defined and generic models are proposed. It is shown that the proposed elements allow a global and consistent flow of configuration activities. A detailed example illustrates the different configuration problems and a discussion terminates the paper.


Configuration Constraint satisfaction problem Product modeling Process modeling Manufacturing process 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aldanondo M., Hadj-Hamou K., Lamothe J. (2004) Mass customization, configuration and manufacturing. CIRP Journal of Manufacturing Systems 33(4): 381–388 (WISU-Verlag Aache)Google Scholar
  2. Aldanondo M., Hadj-Hamou K., Moynard G., Lamothe J. (2003) Mass customization and configuration: Requirement analysis and constraint based modeling propositions. Integrated Computer-Aided Engineering 10(2): 177–189 (IOS Press)Google Scholar
  3. Aldanondo, M., Moynard, G., & Hadj-Hamou, K. (2004). Workload estimation formulae for the deployment of commercial configurators. Proceedings of the International Conference on Economic, Technical and Organizational Aspects of Product Configuration Systems (pp. 119–128). Copenhagen, Denmark.Google Scholar
  4. Bashir H.A., Thomson V. (1999) Metrics for design projects: A review. Design Studies 20(3): 263–277CrossRefGoogle Scholar
  5. Felfernig A., Friedrich G., Jannach D. (2000) UML as domain specific language for the construction of knowledge-based configuration systems. International Journal of Software Engineering and knowledge Engineering 10(4): 449–469CrossRefGoogle Scholar
  6. Fleischanderl G., Friedrich G., Haselböck A., Schreiner H., Stumptner M. (1998) Configuring large systems using generative constraint satisfaction. IEEE Intelligent Systems 13(4): 59–68CrossRefGoogle Scholar
  7. Haag A. (1998) Sales configuration in business processes. IEEE Intelligent Systems 13(4): 78–85CrossRefGoogle Scholar
  8. Hvam, L., Riis, J., & Malis, M. (2002). A multi-perspective approach for the design of configuration systems. Proceedings of the ECAI 2002 Workshop on Configuration (pp. 56–62). Lyon, France.Google Scholar
  9. Hvam, L., Riis, J., Malis, M., & Hansen, B. (2001). Reengineering of the quotation process—application of knowledge based systems procedure for building product models. Proceedings of the 2001 International Conference on Industrial Engineering and Production Management (Vol. 1, pp. 242–248). Quebec City, Canada.Google Scholar
  10. Junker, U., & Mailharro, D. (2003). The Logic of ILOG (J) configurator: Combining constraint programming with a description logic. Proceedings of the IJCAI 2003 Workshop on Configuration (pp. 13–20). Acapulco, Mexico.Google Scholar
  11. Mannisto, T., Peltonen, H., & Sulonen, R. (1996). View to product configuration knowledge modeling and evolution. Proceedings of the AAAI 1996 Workshop on Configuration (pp. 111–118). AAAI Press.Google Scholar
  12. Mittal, S., & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. Proceedings of the 9th National Conference on Artificial Intelligence AAAI (pp. 25–32). Boston, USA.Google Scholar
  13. Mittal, S., & Frayman, F. (1989). Towards a generic model of configuration tasks. Proceedings of IJCAI 1989 (Vol. 2, pp. 1395–1401). Detroit, USA.Google Scholar
  14. Montanari H. (1974) Networks of constraints: Fundamental properties and application to picture processing. Information Sciences 7: 95–132CrossRefGoogle Scholar
  15. Pargamin, B. (2002). Vehicle sales configuration: The cluster tree approach. Proceedings of the ECAI 2002 Workshop on Configuration (pp. 35–40). Lyon, France.Google Scholar
  16. Sabin, D., & Freuder, E. (1996). Configuration as composite constraint satisfaction. Proceedings of the AAAI 1996 Workshop on Configuration (pp. 28–36). AAAI Press.Google Scholar
  17. Sabin, M., & Freuder, E. (1999). Detecting and resolving inconsistency and redundancy in conditional constraint satisfaction problems. Proceedings of AAAI 1999 Workshop on Configuration (pp. 90–94). Orlando, Florida.Google Scholar
  18. Sabin D., Weigel R. (1998) Product configuration frameworks—a survey. IEEE Intelligent Systems 13(4): 42–49CrossRefGoogle Scholar
  19. Soininen, T., & Gelle, E. (1999). Dynamic constraint satisfaction in configuration, Proceedings of AAAI 1999 Workshop on Configuration (pp. 95–100).Google Scholar
  20. Soininen T., Tiihonen T., Männistö T., Sulonen R. (1998) Towards a general ontology of configuration. AIEDAM 12(4): 357–372CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Ecole des Mines d’Albi-CarmauxCentre Génie IndustrielAlbiFrance

Personalised recommendations