You can use Z.bench values to describe the sigma capability of your process. Because they are based on a standard normal distribution, Z.bench statistics are benchmark values that allow you to easily compare process capability.
To understand Z.bench, consider all the defects of a process, which usually fall on either side of the specification limits.
If you put all the defects on the right tail of the distribution, and then measure the number of standard deviations from the center to the point that defines the total defects, you obtain the Z.bench value.
You can perform a normal capability analysis and display Z.bench statistics for continuous data. Suppose the data are in C1, the subgroup size is 5, the lower specification limit is 598, and the upper specification limit is 602. Also assume that the data come from a normal distribution and the process is in statistical control.
Z.bench is often used to estimate the sigma capability of a process. However, the exact method used may differ depending on industry practices or company standards. Some practitioners report sigma capability as the short-term Z.bench value under potential (within) capability, which uses the standard deviation within subgroups. Other practitioners define sigma capability as 1.5 plus the long-term Z.bench value in overall capability, which uses the overall standard deviation of the process. (For example, if the Z.bench under overall capability is 4, the sigma capability is 4 + 1.5 = 5.5.) Therefore, when reporting sigma capability, you should confirm the specific guidelines used in your company or industry.
|Z.bench||Sigma Capability||PPM Defective|
In the table, sigma capability is calculated assuming a Z shift of 1.5σ