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Applying the Mahalanobis–Taguchi System to Improve Tablet PC Production Processes

Author

Listed:
  • Chi-Feng Peng

    (Ph.D. Program of Technology Management, Chung Hua University, Hsinchu 300, Taiwan
    These authors contributed equally to this work.)

  • Li-Hsing Ho

    (Department of Technology Management, Chung-Hua University, Hsinchu 300, Taiwan
    These authors contributed equally to this work.)

  • Sang-Bing Tsai

    (Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
    Economics and Management College, Civil Aviation University of China, Tianjin 300300, China
    These authors contributed equally to this work.)

  • Yin-Cheng Hsiao

    (Department of Technology Management, Chung-Hua University, Hsinchu 300, Taiwan)

  • Yuming Zhai

    (School of Economics and Management, Shanghai Institute of Technology, Shanghai 201418, China)

  • Quan Chen

    (Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China)

  • Li-Chung Chang

    (Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China)

  • Zhiwen Shang

    (Business School, Nankai University, Tianjin 300071, China)

Abstract

Product testing is a critical step in tablet PC manufacturing processes. Purchases of testing equipment and on-site testing personnel increase overall manufacturing costs. In addition, to improve manufacturing capabilities, manufacturers must also produce products with higher quality and at a lower cost than their competitors if they are to attract consumers and gain a competitive edge in their industry. The Mahalanobis–Taguchi System (MTS) is a novel technique proposed by Genichi Taguchi for performing diagnoses and forecasting with multivariate data. The MTS can be used to select important factors and has been applied in numerous engineering fields to improve product and process quality. In the present study, the MTS, logistic regression, and a neural network were used to improve the tablet PC product testing process. The results indicated that the MTS attained 98% predictive power after insignificant test items were eliminated. The MTS performance was superior to those of the conventional logistic regression and neural network, which attained 93.3% and 94.7% predictive power, respectively. After the testing process was improved using the MTS, the number of test items in the tablet PC product testing process was reduced from 56 to 14. This facilitated the development of more stable test site configurations and effectively reduced the testing time, number of testers required, and equipment costs.

Suggested Citation

  • Chi-Feng Peng & Li-Hsing Ho & Sang-Bing Tsai & Yin-Cheng Hsiao & Yuming Zhai & Quan Chen & Li-Chung Chang & Zhiwen Shang, 2017. "Applying the Mahalanobis–Taguchi System to Improve Tablet PC Production Processes," Sustainability, MDPI, vol. 9(9), pages 1-17, September.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:9:p:1557-:d:110624
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    References listed on IDEAS

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    Cited by:

    1. Quan Chen & Sang-Bing Tsai & Yuming Zhai & Chien-Chi Chu & Jie Zhou & Guodong Li & Yuxiang Zheng & Jiangtao Wang & Li-Chung Chang & Chao-Feng Hsu, 2018. "An Empirical Research on Bank Client Credit Assessments," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    2. Hong-Bo Shi & Yong-Cai Cui & Sang-Bing Tsai & Dong-Mei Wang, 2018. "The Impact of Technical–Nontechnical Factors Synergy on Innovation Performance: The Moderating Effect of Talent Flow," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    3. Jackson Jinhong Mi & Zongsheng Huang & Kai Wang & Sang-Bing Tsai & Guodong Li & Jiangtao Wang, 2018. "The Presence of a Powerful Retailer on Dynamic Collecting Closed-Loop Supply Chain From a Sustainable Innovation Perspective," Sustainability, MDPI, vol. 10(7), pages 1-17, June.
    4. Wei Yan & Junwu Chai & Zhifeng Qian & Sang-Bing Tsai & Hong Chen & Yu Xiong, 2018. "Operational Decisions on Remanufacturing Outsourcing Involved with Corporate Environmental and Social Responsibility—A Sustainable Perspective," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    5. Kai Wang & Haomin Zhang & Sang-Bing Tsai & Jin Jiang & Yun Sun & Jiangtao Wang, 2018. "An Empirical Study on Effective Tax Rate and CEO Promotion: Evidence from Local SOEs in China," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    6. Aijun Liu & Xiaohui Ji & Sang-Bing Tsai & Hui Lu & Gang Du & Feng Li & Guodong Li & Jiangtao Wang, 2018. "An Empirical Study on the Innovation Sharing Express Box: Collaborative Consumption and the Overlay Network Design," Sustainability, MDPI, vol. 10(7), pages 1-19, June.
    7. Kai-Cheng Liao & Ming-Yue Yue & Si-Wei Sun & Hong-Bo Xue & Wei Liu & Sang-Bing Tsai & Jiang-Tao Wang, 2018. "An Evaluation of Coupling Coordination between Tourism and Finance," Sustainability, MDPI, vol. 10(7), pages 1-23, July.
    8. Ning Wang & Zhuo Zhang & Jiao Zhao & Dawei Hu, 2022. "Recognition method of equipment state with the FLDA based Mahalanobis–Taguchi system," Annals of Operations Research, Springer, vol. 311(1), pages 417-435, April.

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