Interpretation of Loss Aversion in Kano’s Quality Model

For designing and developing products/services it is vital to know the relevancy of the performance generated by each technical attribute and how they can increase customer satisfaction. Improving the parameters of technical attributes requires financial resources, and the budgets are generally limited. Thus the optimum target can be the achievement of the minimum overall cost for a given satisfaction level. Kano’s quality model classifies the relationships between customer satisfaction and attribute-level performance and indicates that some of the attributes have a non-linear relationship to satisfaction, rather power-function should be used. For the customers’ subjective evaluation these relationships are not deterministic and are uncertain. Also the cost function are uncertain, where the loss aversion of decision makers should be considered as well. This paper proposes a method for fuzzy extension of Kano’s model and presents numerical examples.

[1]  Ming Zhou Fuzzy logic and optimization models for implementing QFD , 1998 .

[2]  Janusz Kacprzyk,et al.  Computational Intelligence in Engineering , 2010 .

[3]  László T. Kóczy,et al.  Fuzzy rule extraction by bacterial memetic algorithms , 2009 .

[4]  János Botzheim,et al.  Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm , 2010, Memetic Comput..

[5]  Herbert Moskowitz,et al.  QFD optimizer: a novice friendly quality function deployment decision support system for optimizing product designs , 1997 .

[6]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[7]  László T. Kóczy,et al.  Comparative Investigation of Various Evolutionary and Memetic Algorithms , 2010 .

[8]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[9]  Kurt Matzler,et al.  The asymmetric relationship between attribute-level performance and overall customer satisfaction: a reconsideration of the importance–performance analysis , 2004 .

[10]  N. Kano,et al.  Attractive Quality and Must-Be Quality , 1984 .

[11]  Stan Lipovetsky,et al.  Customer satisfaction analysis: Identification of key drivers , 2004, Eur. J. Oper. Res..

[12]  J. Hauser,et al.  The House of Quality , 1988 .

[13]  Liang-Hsuan Chen,et al.  An evaluation approach to engineering design in QFD processes using fuzzy goal programming models , 2006, Eur. J. Oper. Res..

[14]  Takeshi Furuhashi,et al.  Fuzzy system parameters discovery by bacterial evolutionary algorithm , 1999, IEEE Trans. Fuzzy Syst..

[15]  Guan-Chun Luh,et al.  ABACTERIAL EVOLUTIONARY ALGORITHM FOR THE JOB SHOP SCHEDULING PROBLEM , 2006 .

[16]  Richard Y. K. Fung,et al.  A new approach to quality function deployment planning with financial consideration , 2002, Comput. Oper. Res..

[17]  Jürgen Bode,et al.  Cost engineering with quality function deployment , 1998 .

[18]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[19]  Kurt Matzler,et al.  How to make product development projects more successful by integrating Kano's model of customer satisfaction into quality function deployment , 1998 .