AbstractThe EWMA chart is an advanced process monitoring tool because of its sensitive nature against small and moderate shifts that occur in the process parameter(s). In this paper, with a transformation related to the Brownian motion, we propose novel EWMA, double EWMA, triple EWMA, and quadruple EWMA charts for monitoring the mean of a process that follows a normal distribution. The Monte Carlo simulations are used to compute the run length properties of the proposed charts. Based on a comparative study, it is found that the newly proposed charts are uniformly and substantially better than their existing counterparts when detecting different kinds of shifts in the process mean. Real datasets are also considered to demonstrate the implementation of the proposed charts.