Process Capability for Non-normal Data– In a Six Sigma project, the defect rate of the process is calculated twice; once during the Define phase to show the impact of the problem, and again during the Control phase to show how the process has improved. Although effective analysis of data that is not distributed normally is possible, completing one of the action steps below is beneficial for some projects to create a more useful data set:
- Divide the data into subsets according to business subprocesses.
- Mathematically transform the data and specification limits.
- Turn the continuous data into discrete data.
The aim of a process capability calculation is to use a sample of items from a process to determine a projection of the number of defects that is expected from the process in the long run. The defect rate, expressed as DPMO (defects per million opportunities), is part of the common language of Six Sigma. Expressing a business problem as a defect rate typically provides a more direct way to communicate with stakeholders and members of the project team.
The process capability calculation is based on:
- The mean of the sample.
- The standard deviation of the sample.
- The known characteristics of the distribution.
The normal, or Gaussian, distribution is commonly observed in data involving a physical measurement of some kind such as length of machined bars, weight of widgets or the average number of manufacturing defects per week. It is less common in a transactional environment when tracking financial information or cycle time.
The flowchart in Figure 1 shows the logic of calculating the process capability starting with a set of continuous data. Note that the subsets referred to in the figure are not the small groups of transactions occurring in close increments of time used to estimate short-term process capability.
Figure 1: Calculating Process Capability with Continuous Data
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