Enter the upper specification limit, lower specification limit, standard deviation, and process mean into the calculator. The CPK calculator will evaluate and display the process capability index of those values.

## CPK Formula

The following formula is used to calculate the CPK, or process capability index, of a given set of data.

CPK = Min [(USL - mean/3 * std.),(mean-LSL/3*std.)]

- Where CPK is the process capability index
- USL is the upper limit
- LSL is the lower limit
- std is the standard deviation

To calculate the CPK, take the minimum value of either of the following values. The upper limit minus the mean divided by three times the standard deviation or the mean minus the lower limit divided by three times the standard deviation.

## CPK Definition

CPK is defined as the process capability index and is a measure of the ability of a process to produce objects to a customer’s specifications.

## Example Problem

**How to calculate CPK? **

The first step in calculating CPK (process capability index) is determining the upper and lower limits of the specifications. In this example, we are looking at a product that needs to be made to certain specifications. The upper limit of one of the dimensions on that product is 5 inches, and the lower limit is 4 inches.

Next, determine the mean of the data. This will be the mean of the dimension through a certain lot of goods. In this case, after 1000 items are produced, the mean is found to be 4.75 inches.

Next, determine the standard deviation of the sample. Using the same 1000-item sample, the standard deviation is calculated to be .3 inches.

Finally, calculate the process capability index using the formula above:

CPK = Min [(USL – mean/3 * std.),(mean-LSL/3*std.)]

CPK = .27777

## FAQ

**What is CPK?**

CPK is a term that standards for process capability index. The process capability index is a value used to measure the ability of a process to produce to a customer’s specifications.

**How do you improve CPK?**

Improve CPK by moving the upper and lower limits closer to the mean.