Skip to main content

Improving the Predictive Validity of Quality Function Deployment by Conjoint Analysis: A Monte Carlo Comparison

  • Conference paper
Operations Research Proceedings 2005

Part of the book series: Operations Research Proceedings ((ORP,volume 2005))

4 Conclusion and outlook

The “new” CA based approach for QFD shows a number of advantages in comparison to the traditional approach. PA importances as well as PC influences on PAs are measured “conjoint” resp. simultaneously. Furthermore, the calculated weights are more precise (real valued instead of 0-, 1-, 3-, or 9-values) which resulted in a higher predictive validity. The Monte Carlo comparison has shown a clear superiority in a huge variety of simulated empirical settings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akao Y (1990) QFD, Integrating customer requirements into product design. Productivity Press, Cambridge, MA

    Google Scholar 

  2. Askin RG, Dawson D (2000) Maximizing customer satisfaction by optimal specification of engineering Characteristics. IIE Transactions 32:9–20

    Article  Google Scholar 

  3. Baier D, Brusch M (2005) Linking quality fuction deployment and conjoint analysis for new product design. In: Baier, D, Decker, R, Schmidt-Thieme, L (eds) Data analysis and decision support. Springer, Berlin, 189–198

    Chapter  Google Scholar 

  4. Baier D, Gaul W (1999) Optimal product positioning based on paired comparison data. Journal of Econometrics 89:365–392

    Article  MATH  Google Scholar 

  5. Baier D, Gaul W (2003) Market simulation using a probabilistic ideal vector model for conjoint data. In: Gustafsson A, Herrmann A, Huber F (eds) Conjoint measurement-methods and applications. 3rd ed., Springer, Berlin, 97–120

    Google Scholar 

  6. Brusch M, Baier D, Treppa A (2002) Conjoint analysis and stimulus presentation: a comparison of alternative methods. In: Jajuga K, Sokolowski A, Bock HH (eds) Classification, clustering, and analysis. Springer, Berlin, 203–210

    Google Scholar 

  7. Cristiano JJ, Liker JK, White CC (2000) Customer-driven product development through Quality Function Deployment in the U.S. and Japan. Journal of Product Innovation Management 17:286–308

    Article  Google Scholar 

  8. Chan LK, Wu ML (2002) Quality Function Deployment: a literature review. European Journal of Operational Research 143:463–497

    Article  MATH  Google Scholar 

  9. Gustafsson A (1996) Customer focused product development by conjoint analysis and Quality Function Deployment. Linköping University Press, Linköping

    Google Scholar 

  10. Hauser JR, Simmie P (1981) Profit maximizing perceptual positions: an integrated theory for the selection of product features and price. Management Science 27:33–56

    Article  Google Scholar 

  11. Pullman ME, Moore WL, Wardell DG (2002) A comparison of Quality Function Deployment and conjoint analysis in new product design. Journal of Product Innovation Management 19:354–364

    Article  Google Scholar 

  12. Urban GL, Hauser JR (1993) Design and marketing of new products. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  13. Yoder B, Mason D (1995) Evaluating QFD relationships through the use of regression analysis. In: Proceedings of the Seventh Symposium on Quality Function Deployment, ASI&GOAL/QPC. American Supplier Institute, Livonia, MI, 239–249

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Baier, D., Brusch, M. (2006). Improving the Predictive Validity of Quality Function Deployment by Conjoint Analysis: A Monte Carlo Comparison. In: Haasis, HD., Kopfer, H., Schönberger, J. (eds) Operations Research Proceedings 2005. Operations Research Proceedings, vol 2005. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-32539-5_97

Download citation

Publish with us

Policies and ethics