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 chart, as an alternative Shewhart c chart. In the 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 nonconforming items. Furthermore, Ohta and Kusukawa presented the chart as an alternative of the CCC-r chart. As a more superior chart in high-yield processes, in this paper we present an chart to detect more sensitively small or moderate shifts in P than the chart. The proposed chart can be constructed by applying the designing method of the chart to the CCC-r chart. ANOS(Average Number of Observations to Signal) of the proposed chart is compared with that of the chart through computer simulation. It is demonstrated from numerical examples that the performance of proposed chart is more superior to the chart.
Keywords
High-yield process; CCC (Cumulative Count of Confirming)-r chart; chart; EWMA(Exponentially Weighted Moving Average) chart; Markov chain approach; ANOS (Average Number of Observations to Signal);
A Synthetic Exponentially Weighted Moving-average Chart for High-yield Processes/ [Kusukawa, Etsuko;Kotani, Takayuki;Ohta, Hiroshi;] / Industrial Engineering and Management Systems