A new S_pk-based sample-size tightening sampling system for lot determination

ABSTRACT

This article presents a new S_pk-based sample-size tightening sampling system by variables inspection for lot determination when the quality characteristic has bilateral specification limits. The proposed system has a flexible switching mechanism between normal and tightened inspection, which is adaptively employed for the occurrence of quality deterioration in submitted lots. The performances of discriminatory power, economy and sensitivity to quality shift of the proposed method are demonstrated by comparing the operating characteristic (OC) curve, average sample number (ASN) and average run length (ARL), respectively, with the existing sampling plans. Furthermore, for practical application, the solved plan parameters under certain quality conditions are tabulated for quick reference, and an example taken from the Multi-layer Ceramic Capacitor (MLCC) industry is studied for illustration.

KEYWORDS:

• two-plan sampling system
• average sample number
• average run length
• operating characteristic curve
• process yield

Disclosure statement

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

Additional information

Funding

This work was partially supported by the National Science and Technology Council (NSTC) of Taiwan under grant number NSTC 110-2221-E-167-028 and NSTC 110-2221-E-035-083-MY2.

Notes on contributors

Shih-Wen Liu

Shih-Wen Liu is currently an assistant Professor in the College of Management at National Chin-Yi University of Technology (NCUT), Taiwan. Dr. Liu received his Ph.D. degree in Industrial Management from National Taiwan University of Science and Technology (NTUST) in 2016. His research interests include quality engineering and management, statistical process control, process capability analysis, applied statistics and data analysis.

Zih-Huei Wang

Zih-Huei Wang is an associate professor in Department of Industrial Engineering and Systems Management at Feng Chia University in Taiwan. Dr. Wang received PhD degree in Industrial Engineering from National Tsing Hua University in 2018. Her research interest mainly focuses on quality engineering and management, process capability analysis, design of experiment and data analysis. She has published some journals and conference papers in recent years. During 2017, she had been a visiting scholar at Georgia Tech in Atlanta and cooperated with Piedmont Heart Institute on the medical image analysis project, and also published two conference papers, one conference poster, and one book chapter in the aforementioned field.