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Purpose: To clarify the different types of data likely to occur in any service or industrial process, the main applicable statistics for each type of data and the Six Sigma metrics that allow characterising and benchmarking organisational processes.Methodology/Approach: A short reference to the statistical process control is carried out, from Shewhart’s works to Motorola’s achievements, followed by a short discussion of the use of Six Sigma tools as a part of today’s total quality approaches, and by a discussion of the continuous, attribute and counting data worlds and their main applications in process analysis. Because many quality professionals may have difficulties dealing with engineering perspectives, a review of main classic and Six Sigma process metrics is done with examples. Complementing discussions, four functions written in the R language are presented, which can deal with real organisational data, or can be used for training purposes.Findings: The functions developed provide useful graphical displays and calculate all necessary metrics, having the ability to let the user provide theoretical values for training activities. Real and simulated case studies help understanding data worlds and respective Six Sigma metrics.Research Limitation/implication: This paper reports an intentionally simple theoretical perspective of Six Sigma metrics and friendly software which is available to all interested professionals on request to the authors.Originality/Value of paper: The paper presents clear definitions of main data types and metrics and is supported by a set of four new functions that can be used by any researcher with a minimum knowledge of the R software.
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