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Fuzzy Testing Method of Process Incapability Index

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  • Kuen-Suan Chen

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
    Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan
    Department of Business Administration, Asia University, Taichung 413305, Taiwan)

  • Tsun-Hung Huang

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Jin-Shyong Lin

    (Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Wen-Yang Kao

    (Office of Physical Education, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Wei Lo

    (School of Business Administration, Guangxi University of Finance and Economics, Nanning 530007, China)

Abstract

The process capability index is a tool for quality measurement and analysis widely used in the industry. It is also a good tool for the sales department to communicate with customers. Although the value of the process capability index can be affected by the accuracy and precision of the process, the index itself cannot be differentiated. Therefore, the process incapability index is directly divided into two items, accuracy and precision, based on the expected value of the Taguchi process loss function. In fact, accuracy and precision are two important reference items for improving the manufacturing process. Thus, the process incapability index is good for evaluating process quality. The process incapability index contains two unknown parameters, so it needs to be estimated with sample data. Since point estimates are subject to misjudgment incurred by the inaccuracy of sampling, and since modern businesses are in the era of rapid response, the size of sampling usually tends to be small. A number of studies have suggested that a fuzzy testing method built on the confidence interval be adopted at this time because it integrates experts and the experience accumulated in the past. In addition to a decrease in the possibility of misjudgment resulting from sampling error, this method can improve the test accuracy. Therefore, based on the confidence interval of the process incapability index, we proposed the fuzzy testing method to assess whether the process capability can attain a necessary level of quality. If the quality level fails to meet the requirement, then an improvement must be made. If the quality level exceeds the requirement, then it is equivalent to excess quality, and a resource transfer must be considered to reduce costs.

Suggested Citation

  • Kuen-Suan Chen & Tsun-Hung Huang & Jin-Shyong Lin & Wen-Yang Kao & Wei Lo, 2024. "Fuzzy Testing Method of Process Incapability Index," Mathematics, MDPI, vol. 12(5), pages 1-11, February.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:623-:d:1342257
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    References listed on IDEAS

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    1. Chen, K.S. & Chen, T.W., 2008. "Multi-process capability plot and fuzzy inference evaluation," International Journal of Production Economics, Elsevier, vol. 111(1), pages 70-79, January.
    2. Dwi Yuli Rakhmawati & Chien-Wei Wu & Chao-Lung Yang, 2016. "Performance evaluation of processes with asymmetric tolerances in the presence of gauge measurement errors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(10), pages 3011-3026, May.
    3. Chirumalla, Koteshwar, 2021. "Building digitally-enabled process innovation in the process industries: A dynamic capabilities approach," Technovation, Elsevier, vol. 105(C).
    4. Chen, K.L. & Chen, K.S. & Li, R.K., 2005. "Suppliers capability and price analysis chart," International Journal of Production Economics, Elsevier, vol. 98(3), pages 315-327, December.
    5. Lepore, A. & Palumbo, B. & Castagliola, P., 2018. "A note on decision making method for product acceptance based on process capability indices Cpk and Cpmk," European Journal of Operational Research, Elsevier, vol. 267(1), pages 393-398.
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