Control Limits
Optimize the Performance of Your Control Charts
Control charts can save you time and money by helping you focus your efforts on only those changes in your process that may indicate serious stability problems.
By default, Minitab uses all of the data in a control chart to calculate the control limits. These lines mark the boundaries between an in-control and out-of-control process. However, there are times when using all the data isn’t the best option. |  |
Extreme values or periods of great instability can have undue influence over the control limits, resulting in a chart that may not identify an out-of-control process. Here are three cases when it makes sense to set the control limits yourself.
Established, historical values
With some longstanding processes you may already know the parameters of a process when it's in control. Likewise, you may have arrived at these values through an earlier control chart made during a period of stability. If you know these parameters it may make sense to use them rather than the parameters generated from the data that are being charted.
| Measurements taken throughout the years have shown the stable mean width of lumber produced in a milling process to be 3.5" with a standard deviation of 0.04", so these values are used to set the control limits. The default limits (based on the sample data) would have missed the out-of-control point in this chart. |  |
Known periods of instability
If you know certain subgroups reflect temporary abnormalities in the process, you may want to leave them out when calculating control limits. Including them can reduce the sensitivity of your control chart. In other words, while it may still be effective in detecting extremes, it may overlook subtler cases of instability.
Points four and five on this chart represent weight measurements taken on a breakfast cereal filling line when a stuck control resulted in overfilled boxes. While included in the chart, these extreme values weren't considered when placing the control limits, so the limits reflect the smaller range of values of the other points. |  |
Stability is reached
Once a process is stable, you can set the control limits so they don't change as you incorporate new data into your chart. This will alert you sooner to gradual process shifts.
| After monitoring the defects produced in a casting process for some time, you're confident the process is stable. You decide to freeze the control limits to ensure they don't drift further apart as you enter new data. | 
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It's worth clarifying that control limits are not the same as specification limits. Specification limits indicate how you'd like your process to perform. Control limits are based on the actual data and indicate out-of-control points.
Remember: just because a process is stable doesn't mean it's performing within your specification requirements. Ensure process stability first, then tackle other process improvements.
Setting control limits in Minitab
There are two simple methods for setting control limits: the best choice depends on your reason for setting them.
Parameters
If you want to fix control limits according to historical values, simply specify the parameters used to calculate the limits (generally the mean and standard deviation).
Suppose you are monitoring the fill weight on a bottling line. The mean and standard deviation of this process are well established, so you decide to use these values. - From the control chart dialog box, choose Xbar-R Options > Parameters.
- In Mean, type 0.92.
- In Standard deviation, type 0.01385.
- Click OK in each dialog box.
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Estimate
Freezing control limits after a certain point or excluding outlier data from the calculation of control limits are both accomplished in the same way: simply include or exclude the desired subgroups (or individual observations, in the case of individuals charts) from the calculation.
Suppose you are monitoring the rate of successful sales calls at a call center. Because this process is new, you'd like to discount the observations in the first three subgroups when calculating the control limits. The operators were trying to familiarize themselves with the new calling scripts, and it was evident at the time that the process was unstable. - From the control chart dialog box, choose P Chart Options > Estimate.
- From the dropdown list, choose Omit the following subgroups when estimating parameters, and type 1:3.
- Click OK in each dialog box.
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Putting it to use
When investigating the stability of a new process, Minitab's default method for establishing control limits in a control chart is a great place to start. But monitoring a process is not a one-time event - it's an ongoing process itself. As you become more familiar with your process, setting your own control limits can be an important part of maintaining process stability.