A forward selection based fuzzy regression for new product development that correlates engineering characteristics with consumer preferences

Fuzzy regression models have commonly been used to correlate engineering characteristics with consumer preferences regarding a new product. Based on the models, product developers can determine optimal engineering characteristics of the new product in order to satisfy consumer preferences. However, they have a common limitation in that they cannot guarantee to include significant regressors with significant engineering characteristics or significant nonlinear terms. The generalization capability of the model can be reduced, when too few significant regressors are included and too many insignificant regressors are included. In this paper, a forward selection based fuzzy regression (FS-FR) is proposed based on the statistical forward selection to determine significant regressors. After the significant regressors are determined, the fuzzy regression is used to generate the fuzzy coefficients which address the uncertainties due to fuzziness and randomness caused by consumer preference evaluations. The developed model includes only significant regressors which attempt to improve the generalization capability. A case study of a tea maker design demonstrated that the FS-FR was able to generate consumer preference models with better generalization capabilities than the other tested fuzzy regressions. Also simpler consumer preference models can be provided for the new product development.

[1]  Y. Chen,et al.  The Hybrid Fuzzy Least-Squares Regression Approach to Modeling Manufacturing Processes , 2008, IEEE Transactions on Fuzzy Systems.

[2]  Yuanyuan Shen,et al.  Multilocus association mapping using generalized ridge logistic regression , 2011, BMC Bioinformatics.

[3]  Kit Yan Chan,et al.  A fuzzy ordinary regression method for modeling customer preference in tea maker design , 2014, Neurocomputing.

[4]  Evangelos Grigoroudis,et al.  The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach , 2008, Eur. J. Oper. Res..

[5]  Ferenc Szeifert,et al.  Genetic programming for the identification of nonlinear input-output models , 2005 .

[6]  Dick den Hertog,et al.  Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming , 2009, IEEE Transactions on Evolutionary Computation.

[7]  Zengqiang Chen,et al.  Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design , 2013, IEEE Transactions on Fuzzy Systems.

[8]  Susan Carlson Skalak.  House of Quality , 2002 .

[9]  Laverne W. Stanton,et al.  Applied Regression Analysis: A Research Tool , 1990 .

[10]  Argon Chen,et al.  Piecewise regression model construction with sample efficient regression tree (SERT) and applications to semiconductor yield analysis , 2012 .

[11]  Yun-Hsi O. Chang.  Hybrid fuzzy least-squares regression analysis and its reliability measures , 2001, Fuzzy Sets Syst..

[12]  Tharam S. Dillon,et al.  An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness , 2011 .

[13]  Sung H. Han,et al.  A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design , 2004 .

[14]  Chia-Jung Chang,et al.  A two-stage approach for formulating fuzzy regression models , 2013, Knowl. Based Syst..

[15]  Amin Safari,et al.  Robust state feedback controller design of STATCOM using chaotic optimization algorithm , 2010 .

[16]  Richard Y. K. Fung,et al.  Estimating the functional relationships for quality function deployment under uncertainties , 2006, Fuzzy Sets Syst..

[17]  J. Watada,et al.  Possibilistic linear systems and their application to the linear regression model , 1988 .

[18]  Lamia Berrah,et al.  Fuzzy handling of measurement errors in instrumentation , 2000, IEEE Trans. Instrum. Meas..

[19]  T. Johnsson,et al.  A procedure for stepwise regression analysis , 1992 .

[20]  Tharam S. Dillon,et al.  Computational Intelligence Techniques for New Product Design , 2012, Studies in Computational Intelligence.

[21]  Inci Batmaz,et al.  Classification models based on Tanaka's fuzzy linear regression approach: The case of customer satisfaction modeling , 2010, J. Intell. Fuzzy Syst..

[22]  Myung Hwan Yun,et al.  Development of customer satisfaction models for automotive interior materials , 2006 .

[23]  Jiafu Tang,et al.  Apply fuzzy regression to model functional relationships in product planning , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[24]  Kit Yan Chan,et al.  A generalised fuzzy least-squares regression approach to modelling relationships in QFD , 2010 .

[25]  Xianyi Zeng,et al.  A fuzzy model of customer satisfaction index in e-commerce , 2008, Math. Comput. Simul..

[26]  Georg Peters.  Fuzzy linear regression with fuzzy intervals , 1994 .

[27]  Ardeshir Bahreininejad,et al.  A hybrid customer prediction system based on multiple forward stepwise logistic regression mode , 2012, Intell. Data Anal..

[28]  Gerald W. Evans,et al.  Fuzzy multicriteria models for quality function deployment , 2000, Eur. J. Oper. Res..

[29]  Dimitris N. Georgiou,et al.  Fuzzy regression analysis: An application on tensile strength of materials and hardness scales , 2012, J. Intell. Fuzzy Syst..

[30]  Abbie Griffin,et al.  The Voice of the Customer , 1993 .

[31]  Richard Y. K. Fung,et al.  Fuzzy regression-based mathematical programming model for quality function deployment , 2004 .