Title: A Bayesian approach for group supplier selections based on the popular process-capability-index C-pk
Authors: Wu, Chin-Chieh
Shiau, Jyh-Jen Horng
Pearn, W. L.
Hung, Hui-Nien
Institute of Statistics
Department of Industrial Engineering and Management
Keywords: Bayesian approach;posterior probability of correct selection;prior information;supplier selection;supply chain management
Issue Date: 2016
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.
URI: http://dx.doi.org/10.1080/16843703.2016.1169674
ISSN: 1684-3703
DOI: 10.1080/16843703.2016.1169674
Volume: 13
Issue: 2
Begin Page: 109
End Page: 123
Appears in Collections:Articles