What is ISO/TR 20693-Implementation of Six Sigma about?
ISO/TR 20693 is a Published Document that discusses statistical methods for implementation of Six Sigma.
ISO/TR 20693 provides guidelines for the identification of distributions related to the implementation of Six Sigma. Examples are given to illustrate the related graphical and numerical procedures.
ISO/TR 20693 considers one-dimensional distribution with one mode. The underlying distribution is either continuous or discrete.
Who is ISO/TR 20693- Implementation of Six Sigma for?
ISO/TR 20693 on statistical methods for implementation of six sigma is beneficial for:
- Data analyst
- Data scientists who works with statistical analysis
- Six Sigma community
- Software package providers
Why should you ISO/TR 20693- Implementation of Six Sigma?
Many statistical techniques assume that the data to be analysed come from a given distribution (or population). Such assumptions are crucial to the effectiveness of subsequent statistical inference methods. In the Six Sigma community, when using such statistical methods, one needs to consider whether this assumption is reasonable. More generally, sometimes it is interesting and necessary to find the distribution which generated the data set (or sample) at hand. Identification of the distribution may provide some ways to answer this question. It consists of finding a distribution (or a family of distributions) which provides a good representation of a sample.
ISO/TR 20 693 can be used to characterize a baseline of the process performance, during the Measure or Analyse phase, to characterise the new process during the Improve phase, and to continuously monitor the process performance during the Control phase to ensure that the change is sustained. From a statistical perspective, ISO/TR 20693 is helpful to find appropriate statistical techniques for the related data since many parametric statistical inference methods need certain distributional assumptions.
ISO/TR 20693 covers distribution identification methods as a tool to:
- Verify that a distribution used historically is still valid for the current data
- Choose the appropriate distribution.
