Robust adaptive exponentially weighted moving average control charts with applications of manufacturing processes

Statistical process control (SPC) has its own importance in the field of quality control. In SPC, control charts are significant tools to monitor process parameters, and exponentially weighted moving average (EWMA) control chart is one such tool. It is a memory-type chart, which is used to target mainly the smaller shifts in the process parameters. Adaptive EWMA (AEWMA) scheme is used to identify small as well as large shifts. EWMA and AEWMA are based on the assumption of normality, which is quite hard to find in practice, and there are many situations where outliers are occasionally present. In the current study, we have proposed four robust adaptive EWMA schemes for monitoring process location parameter. We have investigated their performance under uncontaminated normal and contaminated normal environments. We have carried out comparisons amongst different competing charts based on average run length (ARL), standard deviation of run length (SDRL) and different percentiles of run length distribution. Two examples related to manufacturing processes are also provided for practical implementation of the proposed schemes.