m.s fallahnezhad - Department of Industrial Engineering, Yazd University, Yazd, Iran
v golbafian - Department of Industrial Engineering, Yazd University, Yazd, Iran

In today s highly competitive industrial environment due to fast technology development, quality practitioners will have to detect out-of-control situations and take actions whenever is necessary as soon as possible. Accordingly, new statistical procedures have been enhanced incessantly both to handle high yield processes along with looking for methods of minimizing all quality cost. CCC-r (Cumulative Count Conforming-rarely) chart, the extended method of CCC charts, is commonly applied when nonconforming items are rarely observed. Since the values of the parameters used for the design of the charts control parameters are usually unknown in practice, the practitioners need to estimate them by using an in-control retrospective sample. It has been shown that parameter estimation affects the control charts properties severely. This study develops a model based on estimation costs and Average Number of Inspected Items for CCC-r chart when nonconforming fraction is unknown. The unknown parameters estimated based on different values of sample sizes and sensitivity analysis was performed

Statistical Process Control,High Yield Process,Cumulative Count Conforming-rarely Control Charts,Parameter Estimation,Average Number Inspected,Estimation Costs