Control charts are made to identify assignable causes of difference that could exist in production processes. When traditional control charts are utilized you have the implied presumption that this observations are independently and identically distributed as time passes. It is usually believed the probability distribution which represents the actual observations includes a known functional form and it is constant as time passes. Nevertheless, in reality, observations produced through continuous in addition to discrete generation procedures in many cases are serially correlated. Auto correlation not just breaks the actual independence assumption of conventional control charts, but also can impact the efficiency associated with control charts negatively. In this article, we are going to investigate the result associated with autocorrelation around the performance of MEWMA control chart, in which autocorrelated data were utilized to create the MEWMA chart with induced autocorrelation from various levels of correlations (small, moderate as well as large) and different sample sizes. Simulations had been done to create the data set used to construct the MEWMA control chart and the outcomes implies that all of the control charts constructed had their points outside the designed control limits, that confirmed the effect of autocorrelation to the performance of the MEWMA control chart.
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