In statistics on time series with “moving” in their name, each value is correlated with past and future neighbors — that is, the series is autocorrelated. It affects the way you can use these statistics to detect anomalies and issue alarms.

The moving range in the XmR chart is a case in point. Its autocorrelation in the moving range chart is self-inflicted. It is autocorrelated by construction, regardless of whether the raw data themselves are.

Some raw data are autocorrelated. For example, when you issue a replenishment order for a part by pulling a Kanban from a bin, you are assuming that the demand for a coming period to match that of the period that just elapsed, with minor fluctuations. Implicitly, you are leveraging the autocorrelation of the part consumption across periods.

On the other hand, if a physical characteristic of a manufactured part is the sum of a constant and noise, then the noises are independent, and therefore uncorrelated. Taking moving ranges introduces an autocorrelation between consecutive values that is absent in the raw data.