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control chart interpretation, and other quality metrics.

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Declare the system stable or unstable

Unstable systems

If the system fails any of the tests for control, it is out-of-control or unstable.

If your system is unstable, does the special cause of variation create a favorable output? Does it improve the process? If yes, find the special cause(s) of variation. Try to replicate it and incorporate it into the process.

If not, and it creates a negative output or hurts the process, find the special cause(s) of variation and take steps to eliminate it. After you remove special causes of variation, continue collecting, charting, and analyzing the data. Once the process has been stabilized, consider performing capability analysis to compare how the process is running in relation to its specifications.

Stable systems

If it does not fail any of the tests for control, the process is stable.

If your process is stable, is the data normal distributed? Is it capable of producing output that is within specifications? You may want to create a histogram and perform capability analysis to learn more about your process.

If the process is stable, but failing to meet specification requirements, look for the source(s) of common cause variation. Can these be eliminated or reduced? How can the system be improved?

Note: A process that is currently stable, may not remain stable indefinitely. A new batch of raw material, a new operator, or new equipment, can change the output. Therefore, you should continue collecting, charting, and analyzing data for stable processes.

Capability analysis can be done using software products such as SQCpack.

See also:
>> Analyze for special cause variation
>> When do you recalculate control limits
>> What do the chart pairs mean (variables control charts only)

Quality Advisor

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control chart interpretation, and other quality metrics.

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4 of 5 beyond 1 sigma

Note: Use this test for control sparingly. There is a tendency to overcontrol the system when using this test. Use it only when there is some doubt about the system’s stability.

When four out of five consecutive points lie beyond the 1-sigma limit on one side of the average, the system is declared unstable.

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2 of 3 beyond 2 sigma

Note: Use this test for control sparingly. There is a tendency to overcontrol the system when using this test. Use it only when there is some doubt about the system’s stability.

The control limits drawn on control charts are located three standard deviations away from the average (or center line) of the chart. These are called “3-sigma” control limits. Sigma is the name of the symbol for standard deviation. The distance from the center line to the control limits can be divided into three equal parts, one sigma each, as shown below. If two out of three consecutive points on the same side of the average lie beyond the 2-sigma limits, the system is said to be unstable. The chart below demonstrates this rule.

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Sawtooth

Note: Use this test for control sparingly. There is a tendency to overcontrol the system when using this test. Use it only when there is some doubt about the system’s stability.

The chart below shows a typical sawtooth pattern. Observe how the data points alternate above and below the center line. For some reason, alternate subgroups have greater and smaller averages. Stratifying or splitting the data by key variables may assist in analyzing this problem. This may occur if you alternate samples from two machines or production lines.

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Clusters

Note: Use this test for control sparingly. There is a tendency to overcontrol the system when using this test. Use it only when there is some doubt about the system’s stability.

When clustering is occurring, data appears in groups, even though there is no group of seven points in a row above or below the average. This pattern suggests that the system is “jumping” from one setting to another.

When trying to improve this process, questions should be asked about the transition periods between the clusters. What is causing the system to “jump”?

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Trends

Note: Use this test for control sparingly. There is a tendency to overcontrol the system when using this test. Use it only when there is some doubt about the system’s stability.

Notice that the plot of averages drifts upward on this example, even though there is no group of seven points in a row going up. This pattern indicates a gradual change over time in the characteristic being measured.

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Cycles

When cycles are occurring, the data rises and falls in a rhythmic pattern. The pattern is definitely not random. This could be caused by some regular, periodic change in the system.

A positive aspect of cycles is that they tend to indicate that there is one major cause of variation, which will typically be changing in a similar cyclic fashion. If the cause of the cycle can be established and reduced, this should result in a major improvement to the process.

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Too far from the average

Notice how most of the points in the chart shown below are close to one control limit or the other. This pattern may indicate that subgroups have been drawn from two sources and the data has been mixed—for example, from two machines, two processes, or from two shifts. If this is the case, stratify (separate) the data and re-plot on two charts, or resolve the differences. If the data is not from two sources, the chart may indicate that overcontrolling or tampering is occurring. That is, the process or system is being constantly changed, causing the process to have increased variation.

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control chart interpretation, and other quality metrics.

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Too close to the average

Notice that nearly all the points lie close to the average. This pattern could be caused by a number of circumstances, including:

Edited data
Reduced variability without recalculation of control limits

When this pattern occurs, try to establish why. Is this apparent improvement genuine? Can the improvement be maintained? If the improvement can be maintained, then the control limits need to be recalculated. Although the data looks more stable than normal, this condition is referred to statistically as “unstable”.