<![CDATA[Control charts aim to reduce variability in the process and monitor for out-of- control processes. So far, the process of monitoring quality is usually carried out partially, namely monitoring the mean process and process variability. This approach is less effective and time-consuming because two separate charts must be created simultaneously. One alternative is to analyze both parameters simultaneously, such as through the Progressive Max Chart method (Mixed-Methods Research: Quantitative and Applied). The Progressive Max Chart is a control chart designed for monitoring both the mean and variability by considering the case of subgroup observations. This study uses a quantitative approach, combining primary data collection and simulations to generate findings through statistical analysis and quantifiable measurements. The purpose of this research is to compare methods such as the Progressive Max Chart, EWMA-Max, and Max Chart. The analysis results show that the Progressive Max Chart method performs better than the Max Chart and EWMA- Max Chart, both in terms of mean, variance, and mean-variance detection, for small shifts and large shifts. The control chart performance results provide optimal outcomes for monitoring out-of-control signals at subgroup sizes of n = 2, 3, 5. This is characterized by ARL₁ values that approach 1 more quickly. This method is applied to pH data from vannamei shrimp pond water located in Madura. The Progressive Max Chart method provides optimal results by maximizing the detection of in-control signals. Additionally, it is tested on synthesized data and demonstrates optimal performance in detecting both small and large shifts in mean, variance, and mean-variance.]]>
