Lifetime performance-qualified sampling system under a Weibull distribution with failure-censoring

Abstract

Reducing the warranty cost to increase profit is a major focus for most businesses. To achieve this goal, the acceptance sampling plan (ASP) can be an efficient tool to validate whether the product lifetimes meet their requirements. In the ASP practice, the single sampling plan (SSP) is widely used due to its administrative simplicity. Unfortunately, the SSP ignores the valuable sample information from preceding lots, which may reduce its efficiency and quality-discrimination power. Especially, the collection of lifetime information often consumes a significant amount of time and is expensive in today’s high product-yield and long-life environment. To address the drawbacks of the SSP, we propose a lifetime performance-qualified quick switch sampling (QSS) system under a Weibull distribution with failure-censoring. This system nimbly switches between the normal SSP and tightened SSP based on the previous lot-disposition result. Compared to the ordinary SSP and the recently proposed multiple-lot dependent sampling plan, the QSS system not only significantly reduces the required number of failures that need to be observed in the life testing but also exhibits a better discriminatory operating characteristic curve for the lot disposition. On the other hand, the QSS system helps the consumer to communicate to the supplier important messages that (i) only with reliable submissions the latter can persistently enjoy the benefits of the normal lot-sentencing standard; (ii) once the submitted lot is rejected, the supplier will have to spend more efforts to gain back the consumer trust. Moreover, we developed a web-based tool to help practitioners obtain the system criteria easily and quickly without the need for the traditional time-consuming table lookup. By operating the interactive interface of the web app, practitioners can quickly obtain the system criteria conforming to the requirements. Finally, the proposed QSS system is illustrated by a real-life application.

Keywords:

• failure censoring
• lifetime performance index
• quick switch sampling system
• warranty validation
• Weibull distribution

Additional information

Funding

This work was partially supported by the Ministry of Science and Technology of Taiwan under grant number MOST 107-2221-E-992-064-MY3 and MOST 106-2410-H-230-001-MY2.

Notes on contributors

Ming-Hung Shu

Ming-Hung Shu 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. Ming-Hung completed his PhD in industrial, manufacturing, and system engineering in 1996 and the MS degree in Electrical Engineering in 1993 at the University of Texas, Arlington, USA. He has been awarded as an Outstanding Young Researcher from the Ministry of Science and Technology. His research interests include quality and reliability engineering, decision-making analysis, and applied soft computing.

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 PhD degree in Industrial Engineering and Management with Outstanding Ph.D. Student Award from National Chiao Tung University in 2004 and the MS degree in Statistics from National Tsing Hua University in 2002. He is serving as one of the Editors-in-Chief of Quality Technology and Quantitative Management (QTQM) and editorial board members for various international journals. His research interests include quality engineering and management, statistical process control, process capability analysis, and data analysis.

Bi-Min Hsu

Bi-Min Hsu is an associate professor of Industrial Engineering and Management at Cheng Shiu University, Taiwan. She received a PhD degree in industrial, manufacturing, and system engineering in 2002 at the University of Texas, Arlington, USA. She has long-time joint research with Kaohsiung Chang Gung Memorial Hospital. Her research interests include machine learning, quality and reliability engineering, and applied bioinformatics.

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 PhD 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 management and operations research.