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Short‐run statistical process control: Q ‐chart enhancements and alternative methods
Author(s) -
Castillo Enrique Del,
Montgomery Douglas C.
Publication year - 1994
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.4680100203
Subject(s) - ewma chart , control chart , statistical process control , kalman filter , process (computing) , computer science , variance (accounting) , chart , cusum , control limits , statistics , algorithm , mathematics , accounting , business , operating system
In processes where the length of the production run is short, data to estimate the process parameters and control limits may not be available prior to the start of production, and because of the short run time, traditional methods for establishing control charts cannot be easily applied. Recently, Q charts have been proposed to address this problem. We study the average run length (ARL) of Q charts for a normally distributed variable assuming that a sustained shift occurs in the quality characteristic. It is shown that in some cases Q charts do not exhibit adequate ARL performance. Modifications that enhance the ARL properties of Q charts are presented. Some alternatives to Q charts are also discussed. For the case of a known process target two alternative methods are presented: an exponentially weighted moving average (EWMA) method and an adaptive Kalman filtering method. It is shown that both methods have better ARL performance than Q charts for that case. For the case of both process parameters unknown, an adaptive Kalman filtering method used with a tracking signal provides an ARL performance that improves as better estimates of the process mean and variance are given. A practical example illustrates the tracking signal method for the case when the process parameters are unknown.