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A Z-MR chart is a chart of standardized individual observations (Z) and moving ranges (MR) from a short run process. The chart for individual observations (Z) displays above the chart for moving ranges (MR). Seeing both charts together lets you track both the process level and process variation at the same time. .
Standard control charting techniques rely upon a sufficiently large amount of data to reliably estimate process parameters, such as the process means (m) and process standard deviations (s). Short run processes often do not have enough data in each run to produce good estimates of the process parameters. You can use a single machine or process to produce many different parts, or different products. For example, you may produce only 20 units of a part, then reset the machine to produce a different part in the next run. Even if the runs are large enough to obtain estimates, you would need a separate control chart for each part made by the process, because it is likely that all parts would not have the same mean and standard deviation. Short run charts provide a solution to these problems by pooling and standardizing the data in various ways.
Several methods are commonly used for short runs. The most general method assumes that each part or batch produced by a process has its own unique average and standard deviation. If the average and the standard deviation can be obtained, then the process data can be standardized by subtracting the mean and dividing the result by the standard deviation. The standardized data all come from a population with m = 0 and s = 1. Now you can use a single plot for the standardized data from different parts or products. The resulting control chart has a center line at 0, an upper limit at +3, and a lower limit at -3.
Use Z-MR Chart with short run processes when there is not enough data in each run to produce good estimates of process parameters. Z-MR Chart standardizes the measurement data by subtracting the mean to center the data, then dividing by the standard deviation. Standardizing allows you to evaluate data from different runs by interpreting a single control chart.
You can estimate the mean and process variation from the data various ways, or supply historical values.
