Process Capability with Minitab

What is Process Capability with Minitab?

Process capability in Minitab measures how well a process meets a specified outcome. It indicates whether a process conforms to specified requirements or specifications. Capability analysis helps improve process performance in meeting customer specifications and identify opportunities for process improvement.
Process Capability Analysis Steps

1. Determine the metric or parameter to measure and analyze.
2. Collect the historical data for the parameter of interest.
3. Prove the process is statistically stable (i.e., in control).
4. Calculate the process capability indices.
5. Monitor the process and ensure it remains in control over time. Update the process capability indices if needed.

Process Capability Indices

Process capability can be presented using various indices, depending on the process type and the analysis objective. Popular process capability indices are:

• Cp
• Pp
• Cpk
• Ppk
• Cpm

The Cp index is process capability. It assumes the process mean is centered between the specification limits and essentially is the ratio of the distance between the specification limits to six process standard deviations. Obviously, the higher this value, the better, because it means you can accommodate process variation within the spec limits more easily. Cp measures the process’s potential capability to meet the two-sided specifications. It does not account for the process average.
High Cp indicates a narrow process spread relative to the spread of the customer specifications. Cp is recommended when the process is centered between the specification limits. Cp is valid when both upper and lower specification limits are present. The higher Cp, the better, meaning the process spread is smaller relative to the specification spread.
Note: Cpm only works if a target value is specified.

Use Minitab to Run a Process Capability Analysis

Data File: Capability Analysis” tab in “Sample Data.xlsx

Steps in Minitab to run a process capability analysis:

1. Click Stat → Basic Statistics → Normality Test.
2. A new window named “Normality Test” pops up.
3. Select “HtBk” as the variable.
   
4. Click “OK.”
5. The histogram and normality test results open in a new window.
   

In this example, the p-value is 0.275, greater than the alpha level (0.05). We fail to reject the hypothesis and conclude that the data are normally distributed.

6. Click Stat → Quality Tools → Capability Analysis→ Normal.
7. A new window named “Capability Analysis(Normal Distribution)” pops up.
8. Select “HtBk” as the single column and enter “1” as the subgroup size.
9. Enter “6” as the “Lower spec” and “7” as the “Upper spec.”
   
10. Click the “Options” button, and another new window named “Capability Analysis(Normal Distribution) – Options” pops up.
11. Enter “6.5” as the target and click “OK.”
    
12. Click “OK” in the “Capability Analysis(Normal Distribution)” window.
13. The capability analysis results appear in the new window.
    

If the p-value from the previous normality test is less than the alpha level (0.05), we reject the null hypothesis and conclude that the data are not normally distributed. Thus, we would perform a Non-Normal Capability analysis as follows:

1. Click Stat → Quality Tools → Capability Analysis→ Non-Normal
2. A new window named “Capability Analysis(Non-Normal Distribution)” pops up.
3. Select “HtBk” as the single column.
4. Enter “6” as the “Lower spec” and “7” as the “Upper spec.”
   
5. Click the “Options” button, and another new window named “Capability Analysis(Non-Normal Distribution) – Options” pops up.
6. Enter “6.5” as the target and click “OK.”
   
7. Click “OK” in the “Capability Analysis(Non-Normal Distribution)” window.
8. The capability analysis results appear in the new window.
   

Model summary: With a Ppk of less than 1.0, we can conclude that the capability of this process is not very good. Anything below 1.0 should be considered not capable, and we should strive for Ppk to exceed 1, preferably over 1.67.