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Power analysis for group supplier selection with multiple production lines
Author(s) -
Pearn W.L.,
Tai Y.T.,
Wu YuWen,
Wang YuanAn
Publication year - 2018
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2328
Subject(s) - selection (genetic algorithm) , outsourcing , bonferroni correction , computer science , process capability , sample (material) , production (economics) , production line , sample size determination , reliability engineering , operations research , operations management , work in process , engineering , mathematics , statistics , economics , business , microeconomics , chemistry , chromatography , marketing , artificial intelligence , mechanical engineering
Abstract Group supplier selection problem is a critical problem because outsourcing is an important issue for supply chain management. For the advanced production processes with extremely low fraction of defectives, typical supplier selection methods no longer work because any sample of reasonable size probably contains no defective items. Thus, process capability indices have been extensively used for advanced production processes to evaluate and measure whether the process meets the specifications and they also provide quality assurance. In the globally competitive production environment, processes involving multiple production lines are quite common due to economies of scale considerations. In this paper, we consider a group supplier selection problem for multi‐supplier and multi‐line, in which a supplier group containing the best suppliers is selected based on a multi‐line yield index C pk M . To avoid the inflation of multiple comparisons, we apply the Bonferroni method and subtraction test statistic to tackle the group supplier selection problem. Critical values for testing procedure and required sample sizes for designated selection powers are obtained and tabulated. In addition, we apply multiple comparisons with the best method and compare the selection powers with the Bonferroni method for various power settings to provide more accurate group supplier decisions for real‐world applications.