Designing a variables two-plan sampling system with adjustable acceptance criteria for lot disposition

Abstract

The variables acceptance sampling plan and variables acceptance sampling system (VASS) based on process capability indices (PCIs) have gotten greater attention recently because they can efficiently validate product quality in variables measurement scales. In the VASS, the variables quick-switch sampling system (VQSS) and variables tightened-normal-tightened sampling system (VTSS) are the basic two-plan sampling systems. Compared to the VQSS, the VTSS is more flexible and adaptive because it empowers practitioners to adjust the rule-switch mechanism. Recent studies on the PCI-based VTSS have only focused on the sample size type, denoted as VTSS-I. However, VTSS-I requires a large sample size in the tightened inspection, leading to increased inspection costs and delays in distribution. In this article, we proposed an acceptance criterion-type VTSS, denoted as VTSS-II, based on unilateral PCIs. By comparing the proposed VTSS-II with the existing VTSS-I, the VTSS-II can significantly reduce the inspected sample size and has superior discrimination power. Because the rule-switch mechanism of the VTSS-II is adjustable, we created a cloud-computing app to provide an easy way for practitioners to obtain the optimum system design.

Keywords:

• cloud-computing app
• lot disposition
• Six Sigma
• unilateral specification limit
• variables tightened-normal-tightened sampling system

Additional information

Funding

This work was partially supported by the National Science and Technology Council of Taiwan under grant numbers, NSTC 112-2221-E-013-002-MY3.

Notes on contributors

Chien-Wei Wu

Chien-Wei Wu is currently a Distinguished 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 NTHU in 2002. Dr Wu has received Dr Ta-You Wu Memorial Award (Outstanding Young Researcher Award) from National Science Council (NSC) in 2011, Outstanding Young Industrial Engineer Award from Chinese Institute of Industrial Engineers (CIIE) in 2011, and Outstanding Research Award from the Ministry of Science and Technology (MOST) in 2021. He is also 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.

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.

To-Cheng Wang

To-Cheng Wang received a bachelor’s degree in Aeronautical and Mechanical Engineering from R.O.C. Air Force Academy (ROCAFA), Kaohsiung, Taiwan, and the MS and Ph.D. degrees in Industrial Engineering and Management from the National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan. He is an Assistant Professor in the Department of Aviation Management at ROCAFA. His research interests lie in quality and reliability engineering and operations research.