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Abstract:
Purpose – This paper seeks to propose a univariate robust control chart for location and the necessary table of factors for computing the control limits and the central line as an alternative to the Shewhart X¯ control chart.
Design/methodology/approach – The proposed method is based on two robust estimators, namely, the sample median, MD, to estimate the process mean, µ, and the median absolute deviation from the sample median, MAD, to estimate the process standard deviation, s. A numerical example was given and a simulation study was conducted in order to illustrate the performance of the proposed method and compare it with that of the traditional Shewhart X¯ control chart.
Findings – The proposed robust X¯MDMAD control chart gives better performance than the traditional Shewhart X¯ control chart if the underlying distribution of chance causes is non-normal. It has good properties for heavy-tailed distribution functions and moderate sample sizes and it compares favorably with the traditional Shewhart X¯ control chart.
Originality/value – The most common statistical process control (SPC) tool is the traditional Shewhart X¯ control chart. The chart is used to monitor the process mean based on the assumption that the underlying distribution of the quality characteristic is normal and there is no major contamination due to outliers. The sample mean, X¯, and the sample standard deviation, S, are the most efficient location and scale estimators for the normal distribution often used to construct the X¯ control chart, but the sample mean, X¯, and the sample standard deviation, S, might not be the best choices when one or both assumptions are not met. Therefore, the need for alternatives to the X¯ control chart comes into play. The literature shows that the sample median, MD, and the median absolute deviation from the sample median, MAD, are indeed more resistant to departures from normality and the presence of outliers.
Keywords:
Average run length,
Median,
Statistical process control
Article Type:
Research paper
Article URL:
http://www.emeraldinsight.com/10.1108/02656710910956201
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