The exponential weighted moving average (EWMA) control chart is commonly used in industry to monitor process performance for small changes in objective values. In everyday life, a large amount of data is generated by a technique where a monitoring statistic displays an undefined probability distribution; in this case, nonparametric control charts are used to examine nonconformities from the procedure aim. We aim for an existing nonparametric modified arcsine EWMA (NPMASE) sign control chart in this study. This control chart is accessible based on a single sample; however, the repetitive sampling scheme is less well-known and has received less attention, but it outperforms other sampling schemes. An NPMASE control chart based on repetitive sampling (namely RSMASE) is used here to improve the detectability of small process shifts. The performance of the resulting chart is examined in terms of popular run-length properties such as mean run-length (ARL), median run-length (MDRL), and standard deviation run-length (SDRL). The early RSMASE chart demonstrates the efficiency of shift detection abilities, followed by the NPMASE and nonparametric arcsine EWMA (NPASE) control charts. An actual-life application based on a soft-drink beverage data set for the industrial implementation of the newly designed chart is also explained.