TQM Statistics, Control and the Project Manager

TQM for Project Management- and the PMO

TQM for Project Management- and the PMO


Why Statistics and Control Are Important to the Project Manager

More from the TQM and Project Management [1]

One of the purposes of statistical analysis lies in its ability to discern random variation from non-random (or “controllable”) variation. Random variation is extremely difficult to control, although we have seen situations where variance could be diminished through rigorous use of designed experiments. It is much more common for practitioners to move the mean rather than “fix” the variance.

The TQM project manager will want to understand what factors he or she can control and which factors effectively lie outside the domain of project management. When this awareness manifests, project managers begin to have true control, because they are working with those factors that they can, indeed, influence.

Furthermore, the charts can provide a valuable visual indicator to management about what is really going on during the process. The most difficult part of the statistics and control chart approach is finding project material that is amenable to control charts. In our experience, project managers often treat each project as if it were so unique nothing can be derived from experience.

[1] Pries, K., & Quigley, J. (2012). Statistics and Control. In Total Quality Management for Project Management (p. 110). Boca Raton, FL: Taylor & Francis.

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