Process capability analysis is a set of tools used to find out how well a given process meets a set of specification limits. In other words, it measures how well a process performs.

In practice, it compares the distribution of sample values—representing the process outcome—to the specification limits, which are the limits of what we want achieved. Sometimes it compares to a specification target as well.

Process capability indices are usually used to describe the capability of a process. There are a number of different process capability indices, and whether you calculate one or all may depend on your analysis needs. But to calculate any process capability indices you assume stability of your process; for unstable processes process capability indices are meaningless. So a first step in process capability analysis is a check for stability throughout the process.

An important technique used to determine how well a process meets a set of specification limits is called a process capability analysis. A capability analysis is based on a sample of data taken from a process and usually produces:

1. An estimate of the DPMO (defects per million opportunities).
2. One or more capability indices.
3. An estimate of the Sigma Quality Level at which the process operates.

Capability Analysis for Measurement Data from a Normal Distribution

This procedure performs a capability analysis for data that are assumed to be a random sample from a normal distribution. It calculates capability indices such as Cpk, estimates the DPM (defects per million), and determines the sigma quality level (SQL) at which the process is operating. It can handle two-sided symmetric specification limits, two-sided asymmetric limits, and one-sided limits. Confidence limits for the most common capability indices may also be requested.

Capability Analysis for Measurement Data from Non-Normal Distributions

This procedure performs a capability analysis for data that are not assumed to come from a normal distribution. The program will fit up to 25 alternative distribution and list them according to their goodness-of-fit. For a selected distribution, it then calculates equivalent capability indices, DPM, and the SQL.

Capability Analysis for Correlated Measurements

When the variables that characterize a process are correlated, separately estimating the capability of each may give a badly distorted picture of how well the process is performing. In such cases, it is necessary to estimate the joint probability that one or more variables will be out of spec. This requires fitting a multivariate probability distribution. This procedure calculates capability indices, DPM, and the SQL based on a multivariate normal distribution.

Capability Analysis for Counts or Proportions

When examination of an item or event results in a PASS or FAIL rather than a measurement, the process capability analysis must be based on a discrete distribution. For very large lots, the relevant distribution is the binomial. For small lots or cases of limited opportunities for failure, the hypergeometric distribution must be used: