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Journal of Intelligent Manufacturing

, Volume 23, Issue 4, pp 1419–1431 | Cite as

A network-based assessment approach for change impacts on complex product

  • Hui Cheng
  • Xuening ChuEmail author
Article

Abstract

The complex product design is a continuously changing process from customer requirements to a maturity design. During this process a change of one part will, in most cases, causes changes in other parts and even the whole product. The assessment for the impacts of such changes can support designers’ designing and help manager to manage redesigning. A complex product can be considered as a weighted network of parts, subassemblies, or subsystems. Based on the theory of weighted networks, three changeability indices (degree-changeability, reach-changeability and between-changeability) are presented. Degree-changeability is used to calculate the direct change impacts. Reach-changeability is used to assess the indirectly change impacts because of propagation. If a part influences the other parts dramatically and it is also influenced by them, this part can be predicted by between-changeability. Finally, the three changeability indices are proven to be effective for the change impact assessment through a real-world case of Roots Blowers. With the analysis, the designers can avoid changing to “expensive” parts or subsystems.

Keywords

Complex products Change propagation Networks Change impacts Changeability indices 

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References

  1. Albert R., Barabási A. L. (2002) Statistical mechanics of complex networks. Reviews of Modern Physics 74(1): 47–97CrossRefGoogle Scholar
  2. Bavelas A. (1948) A mathematical model for group structures.  Human Organization 7: 16–30Google Scholar
  3. Boccaletti S., Latora V., Moreno Y., Chavez M., Hwang D. U. (2006) Complex networks: Structure and dynamics. Physics Reports 424(4–5): 175–308CrossRefGoogle Scholar
  4. Clark K. B., Fujimoto T. (1991) Product development performance: Strategy, organization and management in the world auto industry. Harvard Business School Press, BostonGoogle Scholar
  5. Clarkson P. J., Simons C., Eckert C. (2004) Predicting change propagation in complex design. Journal of Mechanical Design 126(9): 788–797CrossRefGoogle Scholar
  6. Cohen T., Navthe S., Fulton R. E. (2000) C-FAR, change favorable representation. Computer Aided Design 32(5): 321–338CrossRefGoogle Scholar
  7. Doreian P. (1974) On the connectivity of social networks. Journal of Mathematical Sociology 3: 245–258CrossRefGoogle Scholar
  8. Eckert, C. M., Zanker, W. & Clarkson, P. J. (2001). Aspects of a better understanding of changes. In Proceedings of international conference on engineering design (ICED’01) (pp. 147–154). Glasgow, UK, 21–23 August.Google Scholar
  9. Eckert C. M., Clarkson P. J., Zanker W. (2004) Change and customization in complex engineering domains. Research in Engineering Design 15(1): 1–21CrossRefGoogle Scholar
  10. Eckert C. M., Keller R., Earl C., Clarkson P. J. (2006) Supporting change processes in design: Complexity, prediction and reliability. Reliability Engineering and System Safety 91(12): 1521–1534CrossRefGoogle Scholar
  11. Flanagan, T. L., Eckert, C. M., Smith, J., Eger, T., & Clarkson, P. J. (2003). A functional analysis of change propagation. In Proceedings of international conference on engineering design (ICED’03) (pp. 441–442). Stockholm, Sweden, 19–21 August.Google Scholar
  12. Freeman L. C. (1977) A set of measures of centrality based on betweenness. Sociometry 40(1): 35–41CrossRefGoogle Scholar
  13. Freeman L. C. (1979) Centrality in social networks: Conceptual clarification. Social Networks 1(3): 215–239CrossRefGoogle Scholar
  14. Giffin, M. L. (2007). Change propagation in large technical systems, Master Thesis, Massachusetts Institute of Technology.Google Scholar
  15. Jarratt, T., Clarkson, P. J., Parks, G. & Eckert, C. M. (2002). Use of Monte Carlo methods in the prediction of change propagation. In Computer-based design: Engineering design conference 2002(EDC’02) (pp. 487–498). London, UK, 9–11 July.Google Scholar
  16. Keller, R., Eckert, C. M. & Clarkson, P. J. (2005). Multiple views to support engineering change management for complex products. In Proceedings of the third international conference on coordinated & multiple views in exploratory visualization (CMV’05) (pp. 33–41). London, UK, 5 July.Google Scholar
  17. Keller, R., et al. (2007). Product models in design: A combined use of two models to assess change risks. In 16th international conference on engineering design (ICED’07) (pp. 673–674). Paris, France, 28–31 August.Google Scholar
  18. Krishnapillai R., Zeid A. (2006) Mapping product design specification for mass customisation. Journal of Intelligent Manufacturing 17(1): 29–43CrossRefGoogle Scholar
  19. Latora V., Marchiori M. (2005) Vulnerability and protection of infrastructure networks. Physical Review E 71(1): 015103(4)CrossRefGoogle Scholar
  20. Lee H. et al (2010) An analytic network process approach to measuring design change impacts in modular products. Journal of Engineering Design 21(1): 75–91CrossRefGoogle Scholar
  21. Martin M. V., Ishii K. (2002) Design for variety: Developing standardized and modularized product platform architectures. Research in Engineering Design 13(4): 213–235Google Scholar
  22. Mikkola J. H., Gassmann O. (2003) Managing modularity of product architectures toward an integrated theory. IEEE Transactions on Engineering Management 50(5): 204–218CrossRefGoogle Scholar
  23. Newman M. E. J. (2003) The structure and function of complex networks. SIAM Review 45(3): 167–256CrossRefGoogle Scholar
  24. Ollinger, G. A. & Stahovich, T. F. (2001). RedesignIT-A constraint-based tool for managing design changes. In Proceedings of DETC ‘01 ASME 2001 design engineering technical conferences and computers and information in engineering conference. Pittsburgh, USA, 9–12 (September), Paper No. DETC2001/DTM-21702.Google Scholar
  25. Pimmler, T. U. & Eppinger, S. D. (1994). Integration analysis of product decompositions. In Proceedings of the ASME design theory and methodology conference (DTM’94) (pp. 343–351). Minneapolis, USA, September.Google Scholar
  26. Reagans R., McEvily B. (2003) Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly 48(2): 240–267CrossRefGoogle Scholar
  27. Simon H. (1996) The sciences of the artificial (3rd ed.). MIT Press, MAGoogle Scholar
  28. Shaw M. E. (1954) Group structure and the behavior of individuals in small groups. Journal of Psychology 38: 139–149CrossRefGoogle Scholar
  29. Strogatz S. H. (2001) Exploring complex networks. Nature 410(3): 268–276CrossRefGoogle Scholar
  30. Suh N. P. (1990) The principles of design. Oxford University Press, New YorkGoogle Scholar
  31. Suh E. S., de Weck O., Kim I., Chang D. (2007) Flexible platform component design under uncertainty. Journal of Intelligent Manufacturing 18(1): 115–126CrossRefGoogle Scholar
  32. Ulrich K., Tung K. (1991) Fundamentals of product modularity. Fundamentals of product modularity. Proceedings of the 1991 ASME winter annual meeting symposium on issues in design/manufacturing integration 39(12): 73–80Google Scholar
  33. Ulrich K. (1995) The role of product architecture in the manufacturing firm. Research Policy 24(3): 419–440CrossRefGoogle Scholar
  34. VanWie M., Stone R. B., Thevenot H., Simpson T. (2007) Examination of platform and differentiating elements in product family Design. Journal of Intelligent Manufacturing 18(1): 77–96CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

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