Conclusion and Future Work

  • Kit Yan Chan
  • C. K. Kwong
  • Tharam S. Dillon
Part of the Studies in Computational Intelligence book series (SCI, volume 403)


In terms of new product development, marketing personnel are usually concerned with making the most of market opportunities by choosing the right price and understanding ‘consumer needs’, while engineering personnel may be concerned only with ascertaining whether the engineering requirements can be met satisfactorily. Product designers are concerned with the product characteristics and appearance of the new product while manufacturing personnel are mainly concerned with the manufacturing process design, quality of manufactured products, and manufacturing time and cost. Therefore, they have different notions about the drivers of success, the optimization variables, and the nature of constraints for new product design. This book has presented and discussed several methodologies for incorporating the concerns of marketing, engineering and manufacturing personnel into new product development.


Customer Satisfaction Functional Model Customer Requirement Importance Weight Design Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Chan, K.Y., Kwong, C.K., Tsim, Y.C.: Improved orthogonal array based simulated annealing with interaction analysis between variables for design optimization. Expert Systems with Applications 36, 7379–7389 (2009a)CrossRefGoogle Scholar
  2. Chan, K.Y., Kwong, C.K., Tsim, Y.C.: A fuzzy nonlinear regression based on genetic programming to modeling manufacturing processes. International Journal of Production Research 48(7), 1967–1982 (2009b)CrossRefGoogle Scholar
  3. Chan, K.Y., Kwong, C.K., Dillon, T.S., Fung, K.Y.: An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness. Journal of Engineering Design 22(3), 523–542 (2010a)Google Scholar
  4. Chan, K.Y., Kwong, C.K., Wong, T.C.: Modelling customer satisfaction for product development using genetic programming. Journal of Engineering Design 22(1), 601–613 (2010b)Google Scholar
  5. Chan, K.Y., Kwong, C.K., Tsim, Y.C., Aydin, M.E., Fogarty, T.C.: A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design. Expert Systems with Applications 37(5), 3853–3862 (2010c)CrossRefGoogle Scholar
  6. Kwong, C.K., Bai, H.: A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of Intelligent Manufacturing 13, 367–377 (2002)CrossRefGoogle Scholar
  7. Kwong, C.K., Bai, H.: Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Transactions 35, 619–626 (2003)CrossRefGoogle Scholar
  8. Kwong, C.K., Wong, T.C., Chan, K.Y.: A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications 36(8), 11262–11270 (2009a)CrossRefGoogle Scholar
  9. Kwong, C.K., Chan, K.Y., Tsim, Y.C.: A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging. Expert Systems with Applications 36, 3829–3838 (2009b)CrossRefGoogle Scholar
  10. Kwong, C.K., Chen, Y., Chan, K.Y., Luo, X.: A generalized fuzzy least-squares regression approach to modelling functional relationships in QFD. Journal of Engineering Design 21(5), 601–613 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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