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New process yield index of asymmetric tolerances for bootstrap method and six sigma approach

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  • Wang, Ching-Hsin
  • Chen, Kuen-Suan

Abstract

With consumers’ stricter requests for product quality, whether the product quality is good or bad has been factored into sustainable development for business sectors. Aiming to increase industrial competitiveness, a number of enterprises have imported the six sigma process one after another and gained remarkable results. However, as product quality is being assessed, it is commonly seen that process specifications are not only symmetric but also asymmetric, and the manufacturing process usually contains unknown parameters. As a result, sampling is needed for estimation whereas the samples do not necessarily have normal distributions. In this study, a new process capability index Yp is proposed for the asymmetric specifications while four types of bootstrap confidence interval methods are applied to find out the bootstrap confidence interval of the indicator Yp, so either the problem of asymmetrical process specifications or the problem of non-normal processes can be solved. In addition, this study applies it to the six sigma process and develops a six sigma model with measurement, analysis, improvement, and control, to promote product quality. From the practical point of view, it can solve the problem of insufficient samples in the industry, and there is an effective evaluation process available for application. Lastly, at the empirical stage, this study takes the manufacturing process of TFT-LCD for example to explain the application of this evaluation process. The evaluation model proposed by this study can provide the related industries with a convenient and practical method when they conduct the six sigma quality improvement and process evaluations.

Suggested Citation

  • Wang, Ching-Hsin & Chen, Kuen-Suan, 2020. "New process yield index of asymmetric tolerances for bootstrap method and six sigma approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 216-223.
  • Handle: RePEc:eee:proeco:v:219:y:2020:i:c:p:216-223
    DOI: 10.1016/j.ijpe.2019.05.004
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    References listed on IDEAS

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    11. 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.
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    Cited by:

    1. Chun-Min Yu & Win-Jet Luo & Ting-Hsin Hsu & Kuei-Kuei Lai, 2020. "Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index," Mathematics, MDPI, vol. 8(12), pages 1-18, November.
    2. Tai-Shan Lee & Ching-Hsin Wang & Chun-Min Yu, 2019. "Fuzzy Evaluation Model for Enhancing E-Learning Systems," Mathematics, MDPI, vol. 7(10), pages 1-11, October.
    3. Jung-Fa Tsai & Chin-Po Wang & Ming-Hua Lin & Shih-Wei Huang, 2021. "Analysis of Key Factors for Supplier Selection in Taiwan’s Thin-Film Transistor Liquid-Crystal Displays Industry," Mathematics, MDPI, vol. 9(4), pages 1-18, February.
    4. Kuen-Suan Chen & Feng-Chia Li & Kuei-Kuei Lai & Jung-Mao Lin, 2022. "Green Outsourcer Selection Model Based on Confidence Interval of PCI for SMT Process," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    5. Kuen-Suan Chen & Chin-Chia Liu & Chi-Han Chen, 2022. "Fuzzy Evaluation of Process Quality with Process Yield Index," Mathematics, MDPI, vol. 10(14), pages 1-11, July.

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