Generalized Fuzzy Least Square Regression for Generating Customer Satisfaction Models

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 403)

Introduction

Chapter 1 mentioned that quality function deployment (QFD) is a commonly used method to support product planning. QFD utilizes four sets of matrices called Houses of Quality (HOQ) to relate customer requirements to product planning, parts deployment, process planning and manufacturing operations (Hauser and Clausing, 1988). In essence, QFD is a systematic and graphical approach, intended to help a design team understand a product’s essential requirements, internal capabilities and constraints, and thereby helps it fulfill customer requirements. Customer requirements acquired from markets are typically qualitative and usually ambiguous in nature, especially for consumer products. Under QFD, customer requirements are mapped into engineering characteristics. Engineering characteristics might not be specific design details or solutions, but they should be measurable. Target values of engineering characteristics, normally housed at the bottom of a HOQ, provide definitive and quantitative technical specifications for new products. This involves a complex decision-making process with multiple variables and in practice, it is normally accomplished in a subjective or heuristic manner.

Keywords

Customer Requirement Quality Function Deployment Fuzzy Regression Fuzzy Interval Fuzzy Linear System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akao, Y.: Quality Function Deployment: Integrating Customer Requirements into Product Design, translated by Glenn Mazur. Productivity Press, Cambridge (1990)Google Scholar
  2. Bai, H., Kwong, C.K.: Inexact genetic algorithm approach to target values setting of engineering requirements in QFD. International Journal of Production Research 41, 3861–3881 (2003)MATHCrossRefGoogle Scholar
  3. Chang, Y.H.O.: Hybrid fuzzy least-squares regression analysis and its reliability measures. Fuzzy Sets and Systems 119, 225–246 (2001)MathSciNetMATHCrossRefGoogle Scholar
  4. Chen, Y., Tang, J., Fung, R.Y.K., Ren, Z.: Fuzzy regression-based mathematical programming model for quality function deployment. International Journal of Production Research 42, 1009–1027 (2004)MATHCrossRefGoogle Scholar
  5. Chen, Y., Chen, L.: A non-linear possibilistic regression approach to model functional relationships in product planning. International Journal of Advanced Manufacturing Technology 28, 11–12, 1175–1181 (2005)CrossRefGoogle Scholar
  6. D’Urso, P., Gastaldi, T.: A least-squares approach to fuzzy linear regression analysis. Computational Statistics and Data Analysis 34, 427–440 (2000)MATHCrossRefGoogle Scholar
  7. Dawson, D., Askin, R.G.: Optimal new product design using quality function deployment with empirical value functions. Quality and Reliability Engineering International 15, 17–32 (1999)CrossRefGoogle Scholar
  8. Diamond, P.: Fuzzy least squares. Information Science 46, 141–157 (1998)MathSciNetCrossRefGoogle Scholar
  9. Fung, R.Y.K., Chen, Y., Tang, J., Tu, Y.: Estimating functional relationships for product planning under uncertainties. Fuzzy sets and Systems 157, 98–120 (2006)MathSciNetMATHCrossRefGoogle Scholar
  10. Fung, R.Y.K., Tang, J.F., Tu, P.Y.L., Chen, Y.Z.: Modeling of quality function deployment planning with resource allocation. Research in Engineering Design 14, 247–255 (2003)CrossRefGoogle Scholar
  11. Hauser, J.R., Clausing, D.: The house of quality, pp. 63–73. Harvard Business Review (1998)Google Scholar
  12. Kim, K.J., Moskowitz, H., Dhingra, A., Evans, G.: Fuzzy multicriteria models for quality function deployment. European Journal of Operational Research 121, 504–518 (2000)MATHCrossRefGoogle Scholar
  13. Kwong, C.K., Chen, Y., Chan, K.Y., Luo, X.: A generalized fuzzy least-squares regression approach to modeling functional relationships in QFD. Journal of Engineering Design 21(5), 601–613 (2010)CrossRefGoogle Scholar
  14. Moskowitz, H., Kim, K.J.: On assessing the H value in fuzzy linear. Fuzzy Sets and Systems 58, 303–327 (1993)MathSciNetMATHCrossRefGoogle Scholar
  15. Moskowitz, H., Kim, K.J.: QFD optimizer: a novice friendly quality function deployment decision support system for optimizing product design. Computers and Industrial Engineering 33, 641–655 (1997)CrossRefGoogle Scholar
  16. Park, T., Kim, K.J.: Determination of an optimal set of design requirements using house of quality. Journal of Operations Management 16, 469–581 (1998)CrossRefGoogle Scholar
  17. Reklaitis, G.V., Ravindran, A., Ragsdell, K.M.: Engineering optimization. John Wiley, NY (1983)Google Scholar
  18. Tanaka, H., Watada, J.: Fuzzy linear systems and their application to the linear regression model. Fuzzy Sets and Systems 27, 275–289 (1988)MathSciNetMATHCrossRefGoogle Scholar
  19. Tang, J., Fung, R.Y.K., Xu, B., Wang, D.: A new approach to quality function deployment planning with financial consideration. Computer and Operations Research 29, 1447–1463 (2002)MATHCrossRefGoogle Scholar
  20. Wang, H.F., Tsaur, R.C.: Insight of a fuzzy regression model. Fuzzy Sets and Systems 112, 355–369 (2000)MathSciNetMATHCrossRefGoogle Scholar
  21. Wassermann, G.S.: On how to prioritize design requirements during the QFD planning process. IIE Transactions 25, 59–65 (1993)CrossRefGoogle Scholar
  22. Xu, R., Li, C.: Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 119, 215–223 (2001)MathSciNetMATHCrossRefGoogle Scholar
  23. Yen, K.K., Ghoshary, S., Roig, G.: A linear regression model using triangular fuzzy number coefficients. Fuzzy Sets and Systems 106, 167–177 (1999)MathSciNetMATHCrossRefGoogle Scholar
  24. Zhou, M.: Fuzzy logic and optimization models for implementing QFD. Computers and Industrial Engineering 35, 237–240 (1998)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

Personalised recommendations