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Developing a multivariate approach to monitor fuzzy quality profiles

Author

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  • Shahram Ghobadi
  • Kazem Noghondarian
  • Rassoul Noorossana
  • S. Mirhosseini

Abstract

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Shahram Ghobadi & Kazem Noghondarian & Rassoul Noorossana & S. Mirhosseini, 2014. "Developing a multivariate approach to monitor fuzzy quality profiles," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 817-836, March.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:2:p:817-836
    DOI: 10.1007/s11135-012-9804-2
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    References listed on IDEAS

    as
    1. Zhang, Jiujun & Li, Zhonghua & Wang, Zhaojun, 2009. "Control chart based on likelihood ratio for monitoring linear profiles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1440-1448, February.
    2. Majid Ahmadabadi & Yaghub Farjami & Mohammad Bameni Moghadam, 2012. "A process control method based on five-parameter generalized lambda distribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(4), pages 1097-1111, June.
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