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Abstract

Statistical Process Control (SPC) is a method used to monitor a process for identifying special causes of variation and necessary to improve the process. One technique commonly used in the SPC is to determine whether the process is stable or not, both the mean and variability. Multivariate control charts are used if necessary to control together two or more related quality characteristics. Sometimes in a process production there is a lack of precision in the calculation, especially if the data used in the form of either data or qualitative attributes. Fuzzy set theory, specifically discusses the development of concepts and techniques related to the sources of uncertainty or imprecision in nature. Control charts are constructed by transforming crisp numbers into fuzzy numbers can be an alternative to obtain representative results of several variables in which there are several quality characteristics. Transformation of some functions, which are used in this study is Fuzzy Median Transformation (FMT). The advantages of FMT is that it can be used for the data in the form of asymmetry. This paper will discuss about the algorithm for  Fuzzy T2  Hotelling control chart and its application to the production data of PT. IGLAS (Persero). From the results of the application of Fuzzy T2  Hotelling control chart got that out of the 5 variables that were analyzed, the dominant variables that lead to out of control is variable bottle molding process

Keywords

Fuzzy Multivariate Control Chart Fuzzy Median Transformation Algorithm Fuzzy T2 Hotelling

Article Details

How to Cite
Kesumawati, A., Mashuri, M., & Irhamah, I. (2014). Fuzzy T2 Hotelling (T_f^2 ) Control Chart. EKSAKTA: Journal of Sciences and Data Analysis, 14(1), 41–51. Retrieved from https://journal.uii.ac.id/Eksakta/article/view/6032

References

  1. Zadeh, L.A., 1965, Fuzzy Sets. Information and Control, 8, 338-359.
  2. Raz, T., & Wang, J., 1990, Probabilistic and Membership Appoaches In The Construction of Control Chart For Linguistic Data. Journal of Production Planning and Control, 1, 147-157.
  3. Taleb, H., Liman, M., & Hirota, K., 2006. Multivariate Fuzzy Multinomial Control Chart. Journal of Quality Technology and Quantitative Management, 3(4), 437-453.
  4. Zarandi, M. H., Fazel., & Turksen, I.B., Kashan, A. H., 2006. Fuzzy Control Charts For Variable And Attribute Quality Characteristics. Journal of Fuzzy Systems, 3(1), 31-44.
  5. Mason, R.L., Young, J.C., & Tracy, N.D., 1999, Improving the Sensitivity of The T2 Statistic in Multivariate Process Control. Journal of Quality Technology , 31(2), 155-165.