Statistical process control (SPC) is a quality control method that enables the monitoring of processes using statistical methodologies. Nonparametric control charts, including the Tukey control chart (TCC), are a robust and effective instrument to assess a method since the actual distribution of the quality characteristic in question is indeterminate. The extended exponentially weighted moving average (EEWMA) control chart was employed to monitor the mean process because of its rapid detection of shifts. To maximize the benefits of both control charts, we developed a method known as EEWMA-TCC, which combines EEWMA with TCC. The efficacy of the proposed chart was evaluated under symmetrical distribution using various individual and aggregate performance metrics based on average run length (ARL) and percentage reduction in ARL (PD_ARL). Our findings indicated that the suggested chart outperforms control charts, including the TCC chart, the EWMA chart, the EEWMA chart, and the EWMA-TCC (mixed exponentially weighted moving average-Tukey) chart, in the quick identification of shifts. An application of the proposed designs in the crucial dimension of machined part data is demonstrated. The results indicated that they were consistent with the research findings. On the other hand, nonparametric control charts provide an alternate way to track the mean process.

Control charts are proposed with the assumption that the process's quality parameter follows a normal distribution. However, in fact, the normality assumption is rarely applied in practice. Parametric charts have a greater false alarm rate and more incorrect out-of-control comparisons in non-normal scenarios. Because the actual distribution of the quality parameter at issue is unknown, nonparametric charts are a strong and useful tool for evaluating a technique. This work proposes a nonparametric mixed exponentially weighted moving average–double moving average chart based on sign statistics to monitor the change in the process's mean under symmetric and asymmetric distributions. The proposed techniques are notable for their efficiency in identifying modest and persistent shifts in the process's location that match the supplied smoothing parameter values. The efficacy of the proposed chart was established through Monte Carlo (MC) simulation utilizing an average run length (ARL), a median run length (MRL), and the standard deviation of run length (S-DRL). Additionally, the average extra quadratic loss (AEQL), performance comparison index (PCI), and relative mean index (RMI) are additional metrics of overall performance that are applied to assess the utility of control charts. The proposed chart is found to be more effective in detecting a small mean shift in the processes faster than alternative charts such as the Shewhart, exponentially weighted moving average (EWMA), moving average (MA), double moving average (DMA), mixed EMMA–MA (MEM), and mixed EWMA–DMA (MEDM) charts under different symmetrical and asymmetrical distributions. In addition, the proposed and existing charts have been applied to three real-life data-sets: (ⅰ) The die-casting hot chamber process used in manufacturing zinc alloy parts for the sanitary industry, (ⅱ) the survival times of a cluster of patients suffering from head and neck cancer disease who were treated with radiotherapy, and (ⅲ) the measurements of the outer diameter at the base of the stem of an exhaust valve bridge.