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Decision-making in testing process performance with fuzzy data

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  • Wu, Chien-Wei

Abstract

Over the years, numerous process capability indices (PCIs) have been proposed to the manufacturing industry to provide numerical measures of process performance. Most research efforts have focused on developing and investigating PCIs that assess process capability by precise measurements of output quality. However, real observations of continuous quantities are not precise numbers; in practice, they are more or less imprecise. Since observations of continuous random variables are imprecise the values of related test statistics become imprecise. Therefore, decision rules for statistical tests have to be adapted to this situation. This article presents a set of confidence intervals that produces triangular fuzzy numbers for the estimation of Cpk index using Buckley's approach with some modification. Additionally, a three-decision testing rule and step-by-step procedure are developed to assess process performance based on fuzzy critical values and fuzzy p-values. This concept is also illustrated with an example for testing process performance.

Suggested Citation

  • Wu, Chien-Wei, 2009. "Decision-making in testing process performance with fuzzy data," European Journal of Operational Research, Elsevier, vol. 193(2), pages 499-509, March.
    • Handle: RePEc:eee:ejores:v:193:y:2009:i:2:p:499-509
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    References listed on IDEAS

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    1. Samuel Kotz & Wen Lea Pearn & N. L. Johnson, 1993. "Some Process Capability Indices are More Reliable than One Might Think," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 55-62, March.
    2. Lee, Hong Tau, 2001. "Cpk index estimation using fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 129(3), pages 683-688, March.
    3. Pearn, W. L. & Wu, Chien-Wei, 2005. "A Bayesian approach for assessing process precision based on multiple samples," European Journal of Operational Research, Elsevier, vol. 165(3), pages 685-695, September.
    4. Wu, Chien-Wei, 2008. "Assessing process capability based on Bayesian approach with subsamples," European Journal of Operational Research, Elsevier, vol. 184(1), pages 207-228, January.
    5. Bernhard Arnold, 1996. "An approach to fuzzy hypothesis testing," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 44(1), pages 119-126, December.
    6. Hong, Dug Hun, 2004. "A note on Cpk index estimation using fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 158(2), pages 529-532, October.
    7. P. Filzmoser & R. Viertl, 2004. "Testing hypotheses with fuzzy data: The fuzzy p-value," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(1), pages 21-29, February.
    8. W L Pearn & Chien-Wei Wu, 2006. "Variables sampling plans with PPM fraction of defectives and process loss consideration," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 450-459, April.
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    Cited by:

    1. Jung-Lin Hung & Cheng-Che Chen & Chun-Mei Lai, 2020. "Possibility Measure of Accepting Statistical Hypothesis," Mathematics, MDPI, vol. 8(4), pages 1-16, April.
    2. Chen, Kuen-Suan & Wang, Ching-Hsin & Tan, Kim-Hua, 2019. "Developing a fuzzy green supplier selection model using six sigma quality indices," International Journal of Production Economics, Elsevier, vol. 212(C), pages 1-7.
    3. Ramli, Azizul Azhar & Watada, Junzo & Pedrycz, Witold, 2011. "Real-time fuzzy regression analysis: A convex hull approach," European Journal of Operational Research, Elsevier, vol. 210(3), pages 606-617, May.
    4. Iván E. Villalón-Turrubiates & Rogelio López-Herrera & Jorge L. García-Alcaraz & José R. Díaz-Reza & Arturo Soto-Cabral & Iván González-Lazalde & Gerardo Grijalva-Avila & José L. Rodríguez-Álvarez, 2022. "A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE," Mathematics, MDPI, vol. 10(16), pages 1-27, August.
    5. Chen, Kuen-Suan & Wang, Ching-Hsin & Tan, Kim Hua & Chiu, Shun-Fung, 2019. "Developing one-sided specification six-sigma fuzzy quality index and testing model to measure the process performance of fuzzy information," International Journal of Production Economics, Elsevier, vol. 208(C), pages 560-565.
    6. Gholamreza Hesamian & Mohamad Ghasem Akbari, 2021. "A process capability index for normal random variable with intuitionistic fuzzy information," Operational Research, Springer, vol. 21(2), pages 951-964, June.

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