To ensure that your results are valid, consider the following guidelines when you collect
data, perform the analysis, and interpret your results.
- The data should be continuous
-
Continuous data are measurements that may potentially take on any numeric
value within a range of values along a continuous scale, including
fractional or decimal values. Common examples include measurements such as
length, weight, and temperature.
If you have attribute data, such as counts of defectives or defects, use
Binomial Capability
Analysis or Poisson Capability
Analysis.
- Collect enough data to obtain reliable estimates of process capability
- Try to collect at least 100 total data points (subgroup size*number of
subgroups), such as 25 subgroups of size 4, or 35 subgroups of size 3. If you do
not collect a sufficient amount of data over a long enough period of time, the
data may not accurately represent different sources of process variation and the
estimates may not indicate the true capability of your process.
- The process must be stable and in control
- If the current process is not stable, then the capability indices cannot be
reliably used to assess the future, ongoing capability of the process. If you
are unsure whether your process is in control, use a control chart to evaluate
process stability before you perform this analysis.
- Use a method that is compatible with process knowledge
- Use process knowledge to confirm the method that the analysis selects. For
example, the Weibull distribution works only with positive data. Suppose that
the analysis selects the Weibull distribution for a process when a sample
happens to have only positive values, but you know that the process routinely
produces negative values. Because the characteristics of the method are not
compatible with the behavior of the process, consider other methods that fit the
data well.
