Developing a skip-lot sampling scheme by variables inspection using repetitive sampling as a reference plan

ABSTRACT

In today’s manufacturing environment, the rate of defective products has been continuously decreasing; thus, variables sampling plans with process capability indices (PCIs) have been recommended to gather more information about a manufacturing process and reduce required sample sizes for inspection. In particular, skip-lot sampling plan (SkSP) is suitable for a series of lots having stable and excellent product quality. Moreover, the concept of repetitive group sampling (RGS), which can allow the use of less samples to maintain desired protection to producers and consumers, is especially appropriate where inspection or testing is costly or destructive. This study, by incorporating the advantages of PCIs, SkSP, and RGS, constructs a variables SkSP with RGS as the reference plan (called SkSP-RGS) based on one-sided PCIs for products with a unilateral specification limit. The proposed plan reduces the sample size while achieving a similar discriminatory power, compared with a conventional variables single sampling plan (SSP), a RGS plan (RGSP), and a SkSP of type 2 (SkSP-2). Tables of plan parameters are provided for frequently applied quality and risk requirements so that practitioners can easily apply the proposed plan.

KEYWORDS:

• Acceptance sampling plan
• process capability indices
• operating characteristic curve
• average sample number
• fraction of nonconformities

Disclosure statement

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

Additional information

Notes on contributors

Chien-Wei Wu

Chien-Wei Wu is currently a Professor in the Department of Industrial Engineering and Engineering Management at National Tsing Hua University (NTHU), Taiwan. Dr. Wu received his Ph.D. degree in Industrial Engineering and Management with Outstanding Ph.D. Student Award from National Chiao Tung University in 2004 and the M.S. degree in Statistics from National Tsing Hua University in 2002. He is serving as one of Editors-in-Chief of Quality Technology and Quantitative Management (QTQM) and editorial board members for several International journals. His research interests include quality engineering and management, statistical process control, process capability analysis and data analysis.

Amy H. I. Lee

Amy H. I. Lee is a Distinguished Professor in the Department of Industrial Management at Chung Hua University, Taiwan. Dr. Lee received the MBA degree from the University of British Columbia, Canada, in 1993 and Ph.D. degree in Industrial Engineering and Management from the National Chiao Tung University, Taiwan, in 2004. Her research interests include performance evaluation, new product development, supply chain management and production management.

Yi-San Huang

Yi-San Huang received her master degree from the Department of Industrial Engineering and Engineering Management at National Tsing Hua University (NTHU), Taiwan, in 2019. Her specific research interests include process capability analysis, quality control and statistical analysis.