A Bayesian approach for group supplier selections based on the popular process-capability-index C_pk

Abstract

Supplier selection is an important part of supply chain management. In the initial stage of production setting, the decision-maker usually faces the problem of selecting the “best” one(s) from available manufacturing material suppliers. For this purpose, process capability indices (PCIs) are commonly used in the literature to rank the suppliers under selection; and among these PCIs, C_pk could be the most popular one. This problem of selecting the best supplier(s) has received considerable attention in the literature but mainly from the frequentist point of view. In this paper, we tackle the so-called group supplier selection problem via the Bayesian approach, namely selecting a group of suppliers that would include the supplier of the largest C_pk value with a high level of confidence in a Bayesian sense. Based on the observed data and available prior information, we develop a practical procedure for the group supplier selection, which is useful for practitioners operating in-plant applications.

Keywords:

• Bayesian approach
• posterior probability of correct selection
• prior information
• supplier selection
• supply chain management

Acknowledgements

The authors thank the Editor and the referees for their valuable comments.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Funding information

Part of this research was supported by the National Science Council in Taiwan, ROC, Grant no. NSC101-2118-M-009-002-MY2.

Notes on contribuors

Chin-Chieh Wu, has completed his PhD degree in Statistics in 2013 from the Department of Statistics at National Chiao Tung University. Currently he is a post-doc researcher in Chang-Gung Memorial Hospital, Linkou. His major area of research interest is Statistical Quality Management and Bio-Statistics.

Jyh-Jen Horng Shiau, is a Professor in the Institute of Statistics at the National Chiao Tung University, Taiwan, where she has been a faculty member since 1992. She holds a BS in Mathematics from the National Taiwan University, Taipei, Taiwan, an MS in Applied Mathematics from the University of Maryland Baltimore County, an MS in Computer Science and a PhD in Statistics from the University of Wisconsin-Madison. Formerly, she taught at Southern Methodist University, the University of Missouri at Columbia, and the National Tsing Hua University and worked for the Engineering Research Center of AT&T Bell Labs before moving to Hsinchu, Taiwan. Her primary research interests include industrial statistics, non-parametric and semiparametric regression, and functional data analysis. She is a lifetime member of the International Chinese Statistical Association.

W. L. Pearn, received the PhD degree in operations research from the University of Maryland, College Park, MD. He is a professor of operations research and quality assurance with National Chiao Tung University (NCTU), Hsinchu, Taiwan. He was with AT&T Bell Laboratories, Murray Hill, NJ, USA, as a Quality Research Scientist before joining NCTU. His current research interests include process capability, network optimization and production management. His publications have appeared in the Journal of the Royal Statistical Society, Series C, Journal of Quality Technology, European Journal of Operational Research, Journal of the Operational Research Society, Operations Research Letters, Omega, Networks and International Journal of Productions Research.

Hui-Nien Hung, is a professor of the Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan. He received a PhD degree in statistics from the University of Chicago, Illinois, in 1996. His research interests include applied probability, biostatistics, statistical computing and industrial statistics.