ABSTRACT Conventionally, the standard process monitoring control charts (s) focused on fixed sample size (). An optimal statistical scheme is proposed in this study using a variable sample size to enhance the performance of a multivariate exponentially weighted moving average () control chart () for compositional data () (i.e. - ) based on a coordinate representation using isometric log-ratio transformation (). A methodology is proposed to obtain the optimal parameters by considering the zero-state () average run length () and the steady-state () average run length () conditions of the process. The statistical performance of the proposed is evaluated based on a continuous-time Markov chain () method for both cases (i.e. the and the ) using a fixed value of in-control (IC) average run length . For benchmarking reasons, the out-of-control () performance of the - is compared against the traditional - with in terms of ; the proposed shows better performance than the - . The and of the - are always less than that of the - at some certain level of shifts. The proposed - performs, on average, () and (for ) more effectively than - . Moreover, it is found that the number of variables (d) has a negative impact on the run length characteristics of the - . When the value of d increases, the and of the - also increase. The of the - is less than the of the - for all the combinations of sample size (n) and d, i.e. under , the proposed performs on average (for d = 3) and (for d = 5) better than the situation. An example of an industrial problem of grid production for a European plant is also given to study the statistical significance and implementation of the - over the existing - .