The R or Range Chart monitors the process variation, or dispersion. The subgroup range (highest point minus the lowest point in the subgroup) is plotted on the R Chart. Like an X-Bar Chart, R Charts have a centerline and two control limits.

The X-Bar & R Combined Control Chart is particularly useful in that it shows changes in mean value and dispersion of the process at the same time, making it a very effective method for checking abnormalities within the process; and if charted while in progress, also points out a problem in the production flow in real time mode.

Control charts (X-Bar and R Charts) are generally used in a production or manufacturing environment and are used to control, monitor and IMPROVE a process. Common causes are always present and generally attributed to machines, material and time vs. temperature. This normally takes a minor adjust tme ent to the process to make the correction and return the process to a normal output. The purpose of drawing a control chart is to detect any changes in the process that would be evident by any abnormal points listed on the graph from the data collected. If these points are plotted in "real time", the operator will immediately see that the point is exceeding one of the contol limits, or is heading in that direction, and can make an immediate adjustment. The operator should also record on the chart the cause of the drift, and what was done to correct the problem bringing the process back into a "state of control".

Where to use it: The purpose of any control chart is to help determine if variations in measurements of a product are caused by small, normal variations that cannot be acted upon ("common causes"), or by some larger "special cause" that can be acted upon or fixed. The type of chart to be used is based on the nature of the data. The X-Bar/R chart is normally used for numerical data that is captured in subgroups in some logical manner – for example 3 production parts measured every hour. A special cause such as a broken tool will then show up as an abnormal pattern of points on the chart.

When to use it:

• When controlling ongoing processes by finding and correcting problems as they occur.
• When predicting the expected range of outcomes from a process.
• When determining whether a process is stable (in statistical control).
• When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process).
• When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.
• Once a process is stable, to monitor its output to make sure that it doesn’t drift or change over time.

How to use it:

1. Choose the appropriate control chart for your data.
2. Determine the appropriate time period for collecting and plotting data. When you start a new control chart, the process may be out of control. If so, the control limits calculated from the first 20 points are conditional limits. When you have at least 20 sequential points from a period when the process is operating in control, recalculate control limits.
3. Collect data, construct your chart and analyze the data.
4. Look for “out-of-control signals” on the control chart. When one is identified, mark it on the chart and investigate the cause. Document how you investigated, what you learned, the cause and how it was corrected.

Important Notes:

• Control charts are used to assess whether a process is stable and in-control; not whether it is in-spec.

Name Format Preview (Click to enlarge)   X-Bar and R Charting Analysis Tool will provide feedback for any out of control or out of specifications from the provided data Microsoft Excel Format   Blank entry template 1 for use on the production floor Microsoft Excel Format   Blank entry template 2 for use on the production floor Adobe PDF Format

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