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Analytical Techniques to Compute \(C_{p}\) and \(C_{pm}\) Capability Indices by R Software

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Intelligent and Fuzzy Techniques: Smart and Innovative Solutions (INFUS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1197))

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Abstract

In statistical quality control, as in other statistical problems, we may be confronted with fuzzy concepts. This paper deals with the problem of process capability estimation, when the observation and the specification limits are fuzzy rather than crisp. In other words, this paper illustrate how a researcher can use “FuzzyNumbers” Package in R software to exactly plot the membership function of capability indices based on fuzzy data and crisp specification limits.

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References

  1. Hsiang, T.C., Taguchi, G.: Tutorial on quality control and assurance-The Taguchi methods. Joint Meetings of the American Statistical Association, Las Vegas, Nevada, p. 188 (1985)

    Google Scholar 

  2. Juran, J.M.: Juran’s Quality Control Handbook, 3rd edn. McGraw-Hill, New York (1974)

    Google Scholar 

  3. Kotz, S.: Process Capability Indices. Chapman and Hall, New York (1993)

    Book  Google Scholar 

  4. Kotz, S., Johnson, N.: Process capability indices - a review, 1992–2000. J. Qual. Technol. 34, 2–19 (2002)

    Article  Google Scholar 

  5. Lee, H.T.: \(C_{pk}\) index estimation using fuzzy numbers. Eur. J. Oper. Res. 129, 683–688 (2001)

    Article  Google Scholar 

  6. Lee, Y.H., Wei, C.C., Chang, C.L.: Fuzzy design of process tolerances to maximize process capability. Int. J. Adv. Manuf. Technol. 15, 655–659 (1999)

    Article  Google Scholar 

  7. Parchami, A., Sadeghpour Gildeh, B.: Trends on process capability indices in fuzzy environment. In: Kahraman, C., Yanik, S. (eds.) Intelligent Decision Making in Quality Management, vol. 97, Chap. 5, pp. 127–140. Springer, Switzerland (2016)

    Google Scholar 

  8. Parchami, A., Mashinchi, M., Sharayei, A.: An effective approach for measuring the capability of manufacturing processes. Prod. Plan. Control. 21(3), 250–257 (2010)

    Article  Google Scholar 

  9. Parchami, A., Sadeghpour Gildeh, B., Nourbakhsh, M., Mashinchi, M.: A new generation of process capability indices based on fuzzy measurements. J. Appl. Stat. 41(5), 1122–1136 (2014)

    Article  MathSciNet  Google Scholar 

  10. Shu, M.H., Wu, H.C.: Quality-based supplier selection and evaluation using fuzzy data. Comput. Ind. Eng. 57, 1072–1079 (2009)

    Article  Google Scholar 

  11. Tsai, C.C., Chen, C.C.: Making decision to evaluate process capability index \(C_{p}\) with fuzzy numbers. Int. J. Adv. Manuf. Technol. 30, 334–339 (2006)

    Article  Google Scholar 

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Correspondence to Abbas Parchami .

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Parchami, A. (2021). Analytical Techniques to Compute \(C_{p}\) and \(C_{pm}\) Capability Indices by R Software. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_164

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