Publication: Monitoring variance by EWMA charts with time varying smoothing parameter
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2016-07
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Abstract
Memory charts like EWMA-S² or CUSUM-S² can be designed to be optimal to detect a
specific shift in the process variance. However, this feature could be a serious
inconvenience since, for instance, if the charts are designed to detect small shift, then,
they can be inefficient to detect moderate or large shifts. In the literature, several
alternatives have been proposed to overcome this limitation, like the use of control
charts with variable parameters or adaptive control charts. This paper proposes new
adaptive EWMA control charts for the dispersion (AEWMA-S²) based on a timevarying
smoothing parameter that takes into account the potential misadjustment in the
process variance. The obtained control charts can be interpreted as a combination of
EWMA control charts designed to be efficient for different shift values. Markov chain
procedures are established to analyse and design the proposed charts. Comparisons with
other adaptive and traditional control charts show the advantages of the proposals.
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Adaptive control charts, Average Run Length, EWMA, CUSUM, Statistical Process Control