In this section, we will explore the three strategies that underlie Visual Six Sigma. We then present the Visual Six Sigma Data Analysis Process that supports these strategies through six steps, and define the Visual Six Sigma Roadmap that expands on three of the key steps. This section closes with guidelines that help you assess your performance as a Visual Six Sigma practitioner.
As mentioned earlier, Visual Six Sigma exploits the following three key strategies to support the goal of managing variation in relation to performance requirements:
Using dynamic visualization to literally see the sources of variation in your data.
Using exploratory data analysis techniques to identify key drivers and models, especially for situations with many variables.
Using confirmatory statistical methods only when the conclusions are not obvious.
Note that with reference to the section titled "Variation and Statistics," Strategy 1 falls within what was called EDA, or statistics as detective. Strategy 3 falls within what we defined as CDA, or statistics as judge. Strategy 2 has aspects of both EDA and CDA.
Earlier, we stressed that by working in the EDA mode of statistics as detective we have to give up the possibility of a neat conceptual and analytical framework. Rather, the proper analysis of our data has to be driven by a set of informal rules or heuristics that allow us to make new, useful ...