How to Calculate Process Capability in Minitab 18

Updated on March 1, 2018
Joshua Crowder profile image

Joshua has work experience in aerospace/aluminum manufacturing & distribution. He received his BBA in accounting from Kent State University.


A capability analysis is used to tell whether a process is capable of producing output to meet customer requirements. The analysis focuses on one single process variable. In the case of the provided example, we will be looking at wire gauge size. This type of analysis can be compared to an older analysis to evaluate whether an improvement effort made the process more capable of meeting customer requirements. The data used for an analysis should come from a relatively stable process and follow a normal or approximately normal distribution. Enough data should be used to ensure precision. Sample data for this tutorial can be found here. If you don't have Minitab 18 you can find a free trial here.

Sample Data Provided in the Download

Completing an Analysis With Minitab

The sample data set provided shows the measurements of a wire 4 times every 2 hours during an 8-hour shift for 8 days straight. There are 8 samples and the sample size is 4. The wire measurement is supposed to be 1.50 +/- .05 per the customer requirement. To create the analysis, go to Statistics >Quality Tools > Capability Analysis > Normal Distribution. We select normal distribution assuming our data follows a normal distribution in this example.

Set Parameters

Place your cursor in the single column box, then highlight C1 and click select. This will add the data that we are analyzing. Next, select subgroup and enter 1. Since the upper an lower tolerance limits are known, add them as well.

Click on the options button to add a title to the graph. Name the title “Process Capability Report for Wire Diameter” and click OK in that window, and OK in the process capability window.

Process Capability Output


The sample mean is very close to the center of our specification range, but shows a slight shift toward the upper limit. My result for Cp is .7. Since this result is less than one we would consider the process unstable. The Cpk is a more relied upon variable and will be lower than Cp. The Cpk for this process is .61 and more than half of what it should be. If Cpk is under 1.33 it is advised that changes may need to be made to the process. In this situation, I only used 34 sample and was not sure if the process was reliable. My next step will be to calculate Cp and Cpk with a larger number of samples and make sure the measuring instrument used to size to inspect the wire is not giving a false reading. As the process stabilizes Cp and Cpk will give better readings as new results are compared to older studies.


Boyer, K. & Verma, R. (2010). Operations & supply chain management for the 21st century. Mason, OH: South-Western.

© 2018 Joshua Crowder


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