Process capability
Capability analysis measures the ability of a process to meet specifications when the process is in statistical control.
A process must be in control before attempting to assess the capability. An out-of-control process is unpredictable and not capable of been characterized by a probability distribution.
Most process capability indices assume a normally distributed quality characteristic. If the distribution is non-normal, it may be possible to transform the data to be normally distributed. The process mean and process sigma define the normal distribution.
- Long-term indices measure the process performance and represent the quality the end-user experiences. They are computed using the process sigma that includes both within-subgroup and between-subgroup variation (the standard deviation of the individual measurements).
- Short-term indices measure the potential process performance ignoring differences between subgroups. They are computed using the process sigma that includes only within-subgroup variation (the Xbar-, R-, S-, or MR- control chart process sigma).
If the process is stable over time, the estimates of short-term sigma and long-term sigma are very similar. They are both estimates of the same parameter, although statistically speaking the long-term sigma is a slightly more efficient estimator.
However, if there are any changes in the process mean over time, the estimate of long-term sigma is greater than that of short-term sigma. The larger the difference between the values of long-term and short-term indices, the more opportunity there is to improve the process by eliminating drift, shifts and other sources of variation.
- Capability ratios (Cp/Pp indices)
Capability ratios (Cp/Pp) describe the variability of a process relative to the specification limits. - Z benchmark
Z benchmark describes the sigma capability of a process. - Nonconforming units
Nonconforming units describe the number of nonconforming units a process produces, expressed in parts per million.