EWMA Chart

Exponentially Weighted Moving Average Chart

Description:

EWMA Charts are typically used when plotting continuous (can apply to attributes data) data to detect small changes over a small period of time. The moving average smoothes the variation of time therefore should not be used when looking for a point that is outside of the process control limits.

The data must be obtained and plotted in sequential order. The data can be in subgroups or individual measurements. Since the data is smoothed it is used to predict performance in the next period of change or instability.

Most statistical software programs the capability and may offer the option to enter various "memory" and weight values. The most recent data point is given the most weight and as time progresses the weight of the older points decreases. The term exponentially means that the weights of the older points decrease exponentially with time. Recall that CUSUM charts use equals weights for previous data points.

These charts are applicable when a I-MR or X-bar & R control chart appears out of control due to wear as would be the case on perishable tooling or dies.

The I-MR and X-bar & R chart are used under the assumption that the mean is constant and observations are independent. Tools and dies are going to wear and shifts in the performance are expected and this may be common cause variation showing up as special cause. When the tool or die is replaced or adjusted, the control chart would exhibit the same pattern as before. This is case where there is correlation between consecutive points and the assumption of indepedent measurements is most likely violated.

In this case, the EWMA chart could show the pattern and make replacement schedules more predictable before there is a failure.

The control limits can be calculated for EWMA charts but the formula is complex (not discussed here) and can be used in estimation or predictor of future performance.

This chart (or other moving average) is sometimes used in stock modeling software packages for analysts trying to predict the next day performance based on the last couple weeks or months of performance. Stock price values are most often not totally independent observations as each day is based on a previous performance and outside factor and are often correlated between successive dates.