In usual quality control methods, the quality of a process or product is evaluated by monitoringone or more quality characteristics using their corresponding distributions. However, when the qualitycharacteristic is defined through the relationship between one or more response and independent variables,the regime is referred to as profiles monitoring. In this article, we improve the performance of theExponentially Weighted Moving Average Range (EWMAR) control charts, which are implemented formonitoring linear profiles (i.e., intercept, slope and average residual between sample and reference lines) byintegrating them with run rules in order to quickly detect various magnitudes of shifts in profile parameters.The validation of the proposed control chart is accomplished by examining its performance using the averagerun length (ARL) criteria. The proposed EWMAR chart with run rules exhibits a much better performance indetecting small and decreasing shifts than the other competing charts. Finally, an example from multivariatemanufacturing industry is employed to illustrate the superiority of the EWMAR chart with run rules.

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