Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics

Kuen-Suan Chena Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung, Taiwan;b Department of Business Administration, Chaoyang University of Technology, Taichung, Taiwan;c Institute of Innovation and Circular Economy, Asia University, Taichung, TaiwanView further author information

Yuan-Lung Laid Department of Industrial Education and Technology, National Changhua University of Education, Changhua, TaiwanView further author information

Ming-Chieh Huangd Department of Industrial Education and Technology, National Changhua University of Education, Changhua, TaiwanView further author information

Tsang-Chuan Change Department of Intelligent Production Engineering, National Taichung University of Science and Technology, Taichung, TaiwanCorrespondence[email protected]

https://orcid.org/0000-0001-8159-3538 View further author information

Abstract

Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performance of various processing characteristics. Products characterise by processing characteristics of both unilateral and bilateral specifications are common in the current sales market. Manufacturing firms must often adopt multiple PCIs to analyze the process performance of a single product, which is inefficient in practical applications and management. Yield-based index $C_{p k}$ is not subject to this limitation. For this reason, we employed $C_{p k}$ to evaluate process performance and the effectiveness of improvement measures. In practice, $C_{p k}$ is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the $100 (1 - α) %$ confidence interval of $C_{p k}$ and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of $C_{p k}$ and develops the fuzzy judgement model.

KEYWORDS:

Acknowledgements

The authors would like to thank the Editor and two anonymous referees for their constructive comments and careful reading, which significantly improved the presentation of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Tsang-Chuan Chang, upon reasonable request.

Additional information

Notes on contributors

Kuen-Suan Chen

Kuen-Suan Chen is currently a Chair Professor of the Department of Industrial Engineering and Management at National Chin-Yi University of Technology, Taiwan, Republic of China. He obtained his Ph.D. degree in Industrial Engineering and Management from the National Chiao Tung University in 1995. His research interests include statistical process control, quality management, process capability analysis, performance evaluation method, and Six Sigma. He has published in International Journal of Production Research, International Journal of Production Economics, Journal of Quality Technology, Quality Engineering, Computers & Industrial Engineering, Journal of Computational and Applied Mathematics, Applied Mathematical Modelling, Annals of Operations Research, etc.

Yuan-Lung Lai

Yuan-Lung Lai received his PhD degree in mechanical engineering from National Chung-Hsing University in 2004. He is currently a professor in the Department of Industrial Education and Technology of National Changhua University of Education, Taiwan. His research interest includes the integration of design and manufacturing in machine tools and intelligent CAD/CAM systems.

Ming-Chieh Huang

Ming-Chieh Huang received the master's degree in industrial engineering and management from National Chin-Yi University of Technology (NCUT), where he is currently pursuing the PhD degree with the Department of Industrial Education and Technology, National Changhua University of Education. His current research interest includes process capability analysis and machining technology.

Tsang-Chuan Chang

Tsang-Chuan Chang is currently an Assistant Professor of the Department of Intelligent Production Engineering, National Taichung University of Science and Technology, Taiwan. He received the PhD degree in the Department of Industrial Management in National Taiwan University of Science and Technology, Taiwan. His current research interests include process capability analysis, Six Sigma methodology and applications, performance evaluation, and quality management. His research has been published in Microelectronics Reliability, Quality Technology and Quantitative Management, Journal of Testing and Evaluation, Journal of Engineering Manufacture, International Journal of Production Research, Journal of Process Mechanical Engineering, etc.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.