Selection of products based on customer preferences applying fuzzy logic

Original Paper

Abstract

Customer satisfaction depends on many variables, such as quality, price, availability, customer service, and so on, and increases with the degree to which the delivered product meets the customer’s preferences. Frequently, vendors have to help customers select a product from among those available to satisfy their needs and wants. Most of the time, the information provided by the customers is not very precise. In this paper, we propose a method to select the product that is the closest to their preferences. A way to measure the relative indifference between different characteristics (Fuzzy Indifference Degree, FID) is proposed as well, which is based on fuzzy preference relations. An example is given to illustrate the applicability of the proposed method.

Keywords

Product evaluation Product selection Customer satisfaction Fuzzy logic Fuzzy preference relation 

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References

  1. 1.
    Chen C.-Y., Chen L.-C., Lin L.: Methods for processing and prioritizing customer demands in variant product design. IIE Trans. 36(3), 203–219 (2004)CrossRefGoogle Scholar
  2. 2.
    Feciková I.: An index method for measurement of customer satisfaction. TQM Mag. 16(1), 57–66 (2004)CrossRefGoogle Scholar
  3. 3.
    Foldesi, P., Koczy, L.T., Botzheim, J.: Fuzzy extension for Kano’s model using bacterial evolutionary algorithm, IEEE. ISCIII’07: Proceedings of the 3rd International Symposium on Computational Intelligence and Intelligent Informatics, pp. 147–151 (2007)Google Scholar
  4. 4.
    Jamali D.: A study of customer satisfaction in the context of a public private partnership. Int. J. Qual. Reliab. Manag. 24(4), 370–385 (2005)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Kuo, Y.-F.: An artificial fuzzy neural controller and its application to customer satisfaction measurement. The University of Texas at Arlington, 129, AAT 9634337 (1996)Google Scholar
  6. 6.
    Kwong C.K., Chen Y., Bai H., Chan D.S.K.: A methodology of determining aggregated importance of engineering characteristics in QFD. Comput. Ind. Eng. 53(4), 667–679 (2007)CrossRefGoogle Scholar
  7. 7.
    Lai X., Xie M., Tan K.-C., Yang B.: Ranking of customer requirements in a competitive environment. Comput. Ind. Eng. 54, 202–214 (2008)CrossRefGoogle Scholar
  8. 8.
    Liu, M.-T.: Fuzzy models for industrial performance and customer satisfaction. The University of Texas at Arlington, 168, AAT 9604010 (1995)Google Scholar
  9. 9.
    Liu, Ch.-H.: A fuzzy multi-factor and attribute decision-making model based on customer survey for product selection. Liu, Chin-Hung, Ph.D., The University of Texas at Arlington, AAT 9718547 (1996)Google Scholar
  10. 10.
    Lin W.-B.: The exploration of customer satisfaction model from a comprehensive perspective. Expert Syst. Appl. 33(1), 110–121 (2007)CrossRefGoogle Scholar
  11. 11.
    Liu X., Zeng X., Xu Y., Koehl L.: A fuzzy model of customer satisfaction index in e-commerce. Math. Comput. Simul. 77(5–6), 512–521 (2008)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Ozer M.: A survey of new product evaluation models. J. Prod. Innov. Manag. 16(1), 77–94 (1999)CrossRefGoogle Scholar
  13. 13.
    Popp H., Lodel D.: Fuzzy techniques and user modeling in sales assistants. User Model. User Adap. Interact. 5(3–4), 349–370 (1995)Google Scholar
  14. 14.
    Tseng T.Y., Klein C.M.: New algorithm for the ranking procedure in fuzzy decision making. IEEE Trans. Syst. Man Cybern. 19(5), 1289–1296 (1989)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Vasant P., Barsoum N.N.: Fuzzy optimization of units products in mix-product selection problem using fuzzy linear programming approach. Soft Comput. 10, 144–151 (2005)CrossRefGoogle Scholar
  16. 16.
    Yuen, K.K.F., Lau, H.C.W.: A distributed fuzzy qualitative evaluation system. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), pp. 560–563 (2006)Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.CIRRELT, Département de Mathématiques et de Génie IndustrielÉcole Polytechnique de MontréalMontréalCanada

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