In industrial environments, achieving high-quality production requires continuous monitoring of processes to reduce waste and increase productivity. Statistical quality control serves as an effective tool for process evaluation, and the control chart, as one of its main tools, plays a significant role in identifying and monitoring process changes. The objective of this study is to evaluate the performance of a hybrid control chart obtained by combining Double Moving Average and Exponentially Weighted Moving Average control charts. To assess the performance of control charts, the run length is first calculated using Monte Carlo simulation. Then, the new metrics of Median Run Length and Standard Deviation of Run Length, alongside the common metric of Average Run Length, are obtained for them, and the capability of control charts in detecting small process shifts based on these new metrics has been evaluated. The results indicate that hybrid control charts are more sensitive than those presented in previous literature in detecting small shifts and represent a more effective tool for enhancing quality and continuous improvement of production processes.