Development of an adaptive sampling system based on a process capability index with flexible switching mechanism

Abstract

The quick-switch sampling system (QSS) and tightened-normal-tightened sampling system (TSS) are efficient schemes for dispositioning a series of lots. However, the QSS mechanism for switching decision rules is too simple to satisfy the requirements of suppliers and buyers. Conversely, the TSS is more flexible due to its adaptable switching mechanism. The TSS was recently developed based on process capability indices (PCIs) to help practitioners make more reliable and accurate decisions in practice. The existing PCI-based TSSs are the required sample-size type (TSS-n). However, the TSS-n requires a large sample size for the tightened inspection, which is costly and time-consuming. We propose the acceptance-benchmark type TSS (TSS-k) based on the most commonly used PCI, to improve the lot-disposition sampling efficiency. The TSS-k adjusts the acceptance benchmark instead of the sample size to constitute tightened and normal inspections. We investigated combinations of TSS-k switching mechanism parameters and provided managerial suggestions for practitioners. Compared with the existing TSS-n, the proposed TSS-k can reduce the average sample number by more than 60% and has superior discrimination power. Moreover, we developed a cloud-computing programme to calculate the optimal system design online. Finally, we illustrate an industrial case to demonstrate the applicability of the proposed TSS-k.

KEYWORDS:

• Tightened-Normal-tightened sampling system
• required sample-size type
• acceptance-benchmark type
• process capability index
• cloud-computing programme

Acknowledgments

The authors would like to thank the associate editor and two anonymous referees for their helpful 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).

Additional information

Funding

This work was supported by National Science and Technology Council, Taiwan: [Grant Number NSTC 111-2222-E-013-001].

Notes on contributors

To-Cheng Wang

To-Cheng Wang received Ph.D. in industrial engineering and management in 2020 at the National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan. He is an Assistant Professor in the Department of Aviation Management at the Republic of China Air Force Academy. His research interests lie in quality and reliability engineering, statistical decision theory, and operations research.

Ming-Hung Shu

Ming-Hung Shu received Ph.D. in industrial, manufacturing, and system engineering in 1996 and an MS degree in Electrical Engineering in 1993 at the University of Texas, Arlington, USA. He is a Professor in Industrial Engineering and Management at the National Kaohsiung University of Science and Technology and an affiliate professor in the Department of Healthcare Administration and Medical Informatics at Kaohsiung Medical University, Taiwan. Prof. Shu has been awarded as an Outstanding Young Researcher and the best yearly research project from the Ministry of Science and Technology. His research interests include quality and reliability engineering, decision-making analysis, and applied soft computing.