Six Sigma methodologies use statistical tools used to transform raw data into information. Based on the results, further actions are taken. Statistical tools and related aspects of Six Sigma methodology comprises about half of Six Sigma. In addition, Six Sigma places a lot of emphasis on graphical interpretation of data collected during the course of measurements.
The importance of statistical methods emanate from the fact that many hypotheses can be disproved with sufficient statistical data. The significance of statistical methods in Six Sigma increases with any increase in sample sizes. The statistical methods quantitatively facilitate evaluation of the performance of any process. The purpose of this being to tackle the cost of poor quality (COPQ) first, Six Sigma has a broader scope than the traditional cost effectiveness model.
Some Important Statistical Methods In Six Sigma
Variations in processes are measured in terms of deviation from the mean and data falling within the acceptable statistical limits. Graphical representation of this data helps companies to visualize things with greater accuracy. Let us examine a few of the most commonly used Six Sigma statistical methods.
Control Chart
The deviations within the acceptable limits (upper & lower) are due to common causes. Anything falling beyond the limits is attributable to some specific cause. For example, take the case of writing your name ten times. Although there are similarities, you probably won’t be able to find any two signatures being exactly the same. The reason is an inherent variation that produces reasonable results within limits and is termed as ‘common cause’. Special causes are those due to forced errors. A control chart has a mathematical mean line in the center and two limit lines. The third component of the Six Sigma control chart is the performance data, which is plotted over time.
You can seek special causes and track common ones through control charts by looking for: