BAYESIAN ESTIMATION AND TEST FOR STATISTICAL PROCESS CAPABILITY WITH MULTIPLE SUBSAMPLES
Process capability indices (PCIs) have been widely used to measure the actual process information with respect to the manufacturing specifications, and become the common language for process quality between the customer and the supplier. Most of existing research works for capability testing are based on the traditional fre quentist point of view and statistical properties of the estimated PCIs are derived based on the assumption of one single sample. In this paper, we consider the problem of estimating and testing process capability using Bayesian statistical techniques based on subsamples collected over time from an in-control process. The posterior proba bility and the credible interval for the most popular index Cp under a non-informative prior are derived. The manufacturers can use the presented approach to perform capability testing and determine whether their proces ses are capable of reproducing product items satisfying customers’stringent quality requirements when a produc tion control plan is implemented for monitoring process stability.
Quality control process capability indices Bayesian inference credible interval lower confidence bounds posterior distribution
Huiming Zhu
College of Business Administration, Hunan University, Changsha 410082, China
国际会议
The Ninth International Conference on Industrial Management(第九届工业管理国际会议 ICIM2008)
日本大阪
英文
969-974
2008-09-16（万方平台首次上网日期，不代表论文的发表时间）