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

Chi-Feng Peng, Li-Hsing Ho, Sang-Bing Tsai, Yin-Cheng Hsiao, Yuming Zhai, Quan Chen, Li-Chung Chang and Zhiwen Shang
Additional contact information
Chi-Feng Peng: Ph.D. Program of Technology Management, Chung Hua University, Hsinchu 300, Taiwan
Li-Hsing Ho: Department of Technology Management, Chung-Hua University, Hsinchu 300, Taiwan
Sang-Bing Tsai: Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
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

Sustainability, 2017, vol. 9, issue 9, 1-17

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.

Keywords: logistic regression; Mahalanobis–Taguchi System (MTS); neural networks; multiple criteria decision making; sustainability; sustainability in manufacturing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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