<p>Advancements in science and technology have significantly transformed various working environments, including manufacturing and healthcare. To ensure processes adhere to precise specifications, it is crucial to quickly and accurately detect assignable causes of variation. This study aims to evaluate and compare two types of exponentially weighted moving average (EWMA) control charts for simultaneous monitoring of both time and magnitude. Specifically, we introduce a new rate-based EWMA chart and compare it with the existing maximum EWMA (Max-EWMA) chart. The rate-based chart uses an exponential distribution to model time and a gamma distribution for magnitude. To assess the performance of these charts, we employ Monte Carlo simulations, analyzing metrics such as the average run length and quartiles of the run length distribution. Additionally, we apply these charting methodologies to real-world data sets. Our findings indicate that the Max-EWMA control chart is more effective in detecting small to medium-sized shifts, particularly with small smoothing parameter values (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\le 0.10\)</EquationSource> </InlineEquation>). In contrast, the EWMA-Rate chart proves to be superior in identifying shifts across all parameters of time and magnitude distributions.</p>