In This Topic
Step 1: Check your data for problems
Important
For a more thorough analysis of these requirements, use Normal Capability Sixpack.
Visually examine the distribution fit
Compare the solid overall curve to the bars of the histogram to assess whether your data are approximately normal. If the bars vary greatly from the curve, your data may not be normal and the capability estimates may not be reliable for your process. If your data appear to be nonnormal, use Individual Distribution Identification to determine whether you need to transform the data or fit a nonnormal distribution to perform capability analysis.
Good fit
Poor fit
Note
You can use the Transform option included in this analysis to transform nonnormal data. To fit a nonnormal distribution to your data, use Nonnormal Capability Analysis.
Compare the within and overall curves
Compare the solid overall curve and the dashed within curve to see how closely they are aligned. A substantial difference between the curves may indicate that the process is not stable or that there is a significant amount of variation between the subgroups. Use a control chart to verify that your process is stable before you perform a capability analysis.
Closely aligned
Poorly aligned
Note
If you are analyzing a process that naturally exhibits large variation between subgroups, such as a batch process, and the variation is not due to special causes, consider using Between/Within Capability Analysis.
Step 2: Examine the observed performance of your process
Use the capability histogram to visually examine the sample observations in relation to the process requirements.
Examine the process spread
Visually examine the data in the histogram in relation to the lower and upper specification limits. Ideally, the spread of the data is narrower than the specification spread, and all the data are inside the specification limits. Data that are outside the specification limits represent nonconforming items.
In this histogram, the process spread is wider than the specification spread, which suggests poor capability. Although most of the data are within the specification limits, there are nonconforming items below the lower specification limit (LSL) and above the upper specification limit (USL).
Note
To determine the actual number of nonconforming items in your process, use the results for PPM < LSL, PPM > USL, and PPM Total. For more information, go to All statistics and graphs.
Assess the center of the process
Evaluate whether the process is centered between the specification limits or at the target value, if you have one. The center of the data occurs at the peak of the distribution curve and is estimated by the sample mean.
In this histogram, although the sample observations fall inside of the specification limits, the peak of the distribution curve is not centered on the target. Most of the data exceed the target value.
Step 3: Evaluate the capability of the process
Use the main capability indices to evaluate how well your process meets requirements.
Assess potential capability
Use Cpk to evaluate the potential capability of your process based on both the process location and the process spread. Potential capability indicates the capability that could be achieved if process shifts and drifts were eliminated.
Generally, higher Cpk values indicate a more capable process. Lower Cpk values indicate that your process may need improvement.
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Compare Cpk with a benchmark that represents the minimum value that is acceptable for your process. Many industries use a benchmark value of 1.33. If Cpk is lower than your benchmark, consider ways to improve your process, such as reducing its variation or shifting its location.
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Compare Cp and Cpk. If Cp and Cpk are approximately equal, then the process is centered between the specification limits. If Cp and Cpk differ, then the process is not centered.
Key Result: Cpk
For these process data, Cpk is 1.09. Because Cpk less than 1.33, the potential capability of the process does not meet requirements. The process is too close to the lower specification limit. The process is not centered, so Cpk does not equal Cp (2.76).
Assess overall capability
Use Ppk to evaluate the overall capability of your process based on both the process location and the process spread. Overall capability indicates the actual performance of your process that your customer experiences over time.
Generally, higher Ppk values indicate a more capable process. Lower Ppk values indicate that your process may need improvement.
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Compare Ppk to a benchmark value that represents the minimum value that is acceptable for your process. Many industries use a benchmark value of 1.33. If Ppk is lower than your benchmark, consider ways to improve your process.
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Compare Pp and Ppk. If Pp and Ppk are approximately equal, then the process is centered between the specification limits. If Pp and Ppk differ, then the process is not centered.
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Compare Ppk and Cpk. When a process is in statistical control, Ppk and Cpk are approximately equal. The difference between Ppk and Cpk represents the improvement in process capability that you could expect if shifts and drifts in the process were eliminated.
Key Result: Ppk
For these process data, Ppk = 0.52. Because Ppk is less than 1.33, the overall capability of the process does not meet requirements. The process is centered, so Ppk ≈ Pp (0.53). However, Ppk < Cpk (0.72), which indicates that the overall capability of the process could be improved if the shifts and drifts (the variation between the subgroups) were reduced.
Important
The Cpk and Ppk indices measure the capability of the process in relation only to the specification limit that is closest to the process mean. Therefore, these indices represent only one side of the process curve, and do not measure how the process performs on the other side of the process curve. If your process produces nonconforming items that fall outside both the lower and upper specification limits, use additional capability measures in the output to more fully assess process performance. For more information on other capability measures, go to All statistics and graphs.
