Bayesian Analysis of Deming''s Inspection Model using Variables Sampling

• Main Authors: Hui-Wen Chen, 陳慧雯
• Other Authors: Chiuh-Cheng Chuy
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/94726223650134138523

碩士 === 元智大學 === 工業工程與管理學系 === 91 === This thesis studies a sampling inspection model discussed by Deming that can be applied to a situation where the quality characteristic measurement of items is on a continuous scale. In previous studies the model was discussed on the case of attributes sampling plan; i.e., sampling information is concerning the number of items being conforming or nonconforming. This study develops a variable sampling plan under the framework of Deming’s model using Bayesian approach with the objective to minimize the Bayes’ risk. We first derive the model from a general point of view and then discuss the model for two particular cases: one is double-sided specification limits with component performance assumed to be normal distribution and the other is single-sided specification limit with exponential distribution. For both cases the unknown parameter is mean. The models by attributes for both cases under the same probability model and cost structure are also derived and analyzed. In the numerical result we find that the sampling model by variables needs less samples and produces less cost than the model by attributes. We also conclude that the expected total cost of the model is smaller under Bayesian approach as compared to directly applying the Deming’s rule with classical approach. Finally, a sensitivity analysis on an example is presented in order to realize the effects of model parameters such as inspection cost, prior distribution, and lot size on the total cost and the optimal sample size of the problem.