Integration of the variance of quadratic loss for evaluating process performance

Abstract

This article interprets Taguchi’s quadratic quality loss function from a different viewpoint by considering the variance of quadratic loss as well as the mean of quadratic loss. The behavior of the variance of quadratic loss is characterized by the kurtosis and variance of the quality characteristic. To evaluate the location and dispersion performances of the quadratic loss simultaneously, a distance method linked with a Pareto front approach is proposed for process performance evaluation.

Keywords:

• quadratic loss
• variance of quadratic loss
• kurtosis
• distance method
• Pareto front approach
• performance evaluation

Acknowledgment

We thank the editor and referees for their valuable comments and suggestions.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC 71871119, 71702072), the Natural Science Foundation for Jiangsu Institutions (BK20170810), and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2017R1A2B4004169, NRF-2018R1D1A1B07049764), which are gratefully acknowledged.

Notes on contributors

Yanjing Zhang

Yanjing Zhang is currently a PhD student in the Department of Management Science and Engineering at Nanjing University of Science and Technology of China. She received her B.S. in Physics and Chemistry from Xihua University, China. Her research interests include quality and reliability engineering.

Yizhong Ma

Yizhong Ma is a professor in the Department of Management Science and Engineering at Nanjing University of Science and Technology. He received his BS in Applied Mathematics from Huazhong Normal University, Wuhan China, and his MS in Quality Engineering and PhD in Control Science from Northwestern Polytechnical University, Xi’an, China. He is also assigned as the Director of Quality Society of China, and the Expert Member of Six Sigma Promotion Committee in China. His research interest includes quality engineering and quality management.

Chanseok Park

Chanseok Park is Professor of Industrial Engineering at Pusan National University, Korea. Before joining Pusan National University, he was a faculty member of Mathematical Sciences at Clemson University, Clemson SC, USA from 2001 to 2015. He received his BS in Mechanical Engineering from Seoul National University, MA in Mathematics from the University of Texas at Austin, and his PhD in Statistics from the Pennsylvania State University. He conducts various research on quality and reliability engineering, competing risks model, robust inference, and solid mechanics.

Jai-Hyun Byun

Jai-Hyun Byun is Professor of Industrial and Systems Engineering at Gyeongsang National University, Korea. He received his BS in Industrial Engineering from Seoul National University, and both MS and PhD in Industrial Engineering from Korea Advanced Institute of Science and Technology, Korea. His main research interest is in the field of design of experiments and quality management , engineering, and data analytics engineering.