Exponentially Weighted Moving Average Chart for High-Yield Processes

Borror et al. discussed the EWMA(Exponentially Weighted Moving Average) chart to monitor the count of defects which follows the Poisson distribution, referred to the EWMAc chart, as an alternative Shewhart c chart. In the EWMAc chart, the Markov chain approach is used to calculate the ARL (Average Run Length). On the other hand, in order to monitor the process fraction defectives P in high-yield processes, Xie et al. presented the CCC(Cumulative Count of Conforming)-r chart of which quality characteristic is the cumulative count of conforming item inspected until observing r(≥2) nonconforming items. Furthermore, Ohta and Kusukawa presented the CS(Confirmation Sample)CCC-r chart as an alternative of the CCC-r chart. As a more superior chart in high-yield processes, in this paper we present an EWMACCC-r chart to detect more sensitively small or moderate shifts in P than the CSCCC-r chart. The proposed EWMACCC-r chart can be constructed by applying the designing method of the EWMAc chart to the CCC-r chart. ANOS(Average Number of Observations to Signal) of the proposed chart is compared with that of the CSCCC-r chart through computer simulation. It is demonstrated from numerical examples that the performance of proposed chart is more superior to the CSCCC-r chart.