The Shewhart and the Bonferroni-adjustment S control charts are usually applied to monitor the standard deviation of a quality characteristic. The control limits of these charts are constructed using approximately the normal distribution in case that the standard deviation parameter is known or unknown. In this paper, we establish a new S chart that is based approximately on the normal distribu...

When a manufacturing process is subject to random shocks, detecting the changes in the process and adjusting an out-of-target process are two essential functions of process quality control. Traditional SPC techniques emphasize process change detection, but do not provide an explicit process adjustment method. This paper discusses a general sequential adjustment procedure based on Stochastic App...

In most real-world applications, such as production and manufacturing processes, the underlying process distribution does not always follow a normal distribution. cases, statistical control literature recommends use of nonparametric (or distribution-free) charts. This paper introduces new distribution-free precedence chart using repetitive sampling. The performance proposed is investigated in t...

Statistical process control (SPC) requires statistical methodologies that detect changes in the pattern of data over time. The common methodologies, such as Shewhart, cumulative sum (cusum), and exponentially weighted moving average (EWMA) charting, require the in-control values of the process parameters, but these are rarely known accurately. Using estimated parameters, the run length behavior...

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This paper develops a new nonparametric methodology for monitoring location parame...

Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must...