Titlebook: Statistical Models and Control Charts for High-Quality Processes; M Xie,T N Goh,V Kuralmani Book 2002 Springer Science+Business Media New

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Control Charts with Probability Limits,ts. In a near zero-defect environment, many problems indicated in Section 1.2 will arise and the traditional control charts with 3-sigma limits cannot be interpreted in the same way as the actual false alarm probability could be very different. Probability limits can be obtained by fixing the false

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Cumulative Count of Conforming (,) Chart,nd given the name . chart in Goh (1987). This is a powerful technique for process control when a large number of consecutive conforming items are observed between two nonconforming ones. A control chart can be set up to monitor this number and decisions made based on whether this number is too large

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Process Improvement Detection,stical process control tools would find themselves inadequate in providing the needed decision support. New statistical analysis techniques and decision making procedures are therefore continually been evolved, both to handle high performance processes as well as to look for process improvement oppo

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, and , Procedures,ty of false alarms. Also, small shifts in the process characteristics, which are of increasing importance from the point of view of efforts for continuous quality improvement, cannot be detected easily. Cumulative sum (.) and exponentially weighted moving average (.) charts are widely recommended. A

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