I returned, and saw that under the sun, that the race is not to the swift, nor the battle to the strong, neither yet riches to men of understanding, nor yet favor to men of skill; but time and chance happeneth to them all.
(Ecclesiastes 9:11)
A project system attempts to predict and produce a certain result for a certain cost by a certain time. As the quote from Ecclesiastes illustrates, people know full well that the world is an uncertain place. Variation exists everywhere. Predictions are never completely accurate.
Understanding variation is essential to making any real system oper- ate. Popper, in an essay titled “Of Clouds and Clocks,” described a range of reality fundamental to understanding variation [7]. He bids us to consider a horizontal line, with a clock on the right representing the ultimate of a clockwork-like deterministic world. In that world, everything would eventually be completely predictable; it is only a matter of understanding completely the cause-effect relationships that determine the workings of this mechanical model. The ultimate manifestation of this model is the
working of the planets of the solar system, whose motions are predictable with uncanny accuracy using the equations defined by Isaac Newton.
The cloud, at the other extreme of Popper’s continuum, represents complete chaos—not the deterministic chaos of current mathematics, but the random chaos associated with the world of complete uncertainty. It represents the unpredictability of science at the quantum level and the unpredictability of nature at the human scale. Popper wrote, “My clouds are intended to represent physical systems which, like gases, are highly irregular, disorderly, and more or less unpredictable.” Everything falls between those two extremes.
Uncertainty means indefinite, indeterminate, and not certain to occur, problematical, not known beyond doubt, or not constant. All predictions are uncertain. Fundamental physics tells us that all knowl- edge of reality is uncertain; the better we know the position of something, the less we know about how fast it is moving. Uncertainty is the true state of the world.
Project managers can predict many things well enough to achieve the things they plan, such as building a house. Scientists also know that we can never accurately predict certain other things. For example, no matter how well we learn to model the weather, and how well we measure con- ditions at one point in time to run the model, our ability to predict specific phenomena will always be limited by the nature of the physical laws that determine local weather behavior. Scientists now know (from the chaos theory) that they will never be able to predict when and where the next tornado will touch down. On the other hand, they can predict seasonal trends reasonably well.
Starting in the seventeenth century, mathematicians and scientists have sought to improve the ability to predict the world further and fur- ther over into the cloudy region. At the same time, science kept moving the cloudy region to include more and more of nature. It extended the smallest scale with quantum mechanics, and showed cloudiness at the largest scale with increasing understanding of the universe. Cloudi- ness encompassed all intermediate scales with the discovery of chaos and study of complex adaptive systems.
2.2.2.1 Common and special cause variation
Probability and statistics are science’s weapons of choice to deal with cloudy systems. Shewhart [8], a mentor to Dr. Deming, identified the
need to operate systems in a state of statistical control to have a degree of predictability. He observed, “Every mathematical theorem involving this mathematically undefined concept [statistical control] can then be given the following predictive form: If you do so and so, then such and such will happen.”
Following Shewhart, Deming emphasized the importance of distin- guishing between common cause variation and special cause variation. It is necessary to distinguish between them to get a system under statistical control. It is necessary to have a system under statistical control to predict its future performance. Common cause variation is variation within the capability of a system to repeatedly produce results. Special cause variation is variation beyond that range; usually variation with causes outside the system. Management’s function is to improve the system while avoiding two mistakes:
Mistake 1: Treating common cause variation as if it were special cause variation;
Mistake 2: Treating special cause variation as if it were common cause variation.
Dr. Deming called mistake 1 “tampering.” Tampering is making changes within a system that is operating in statistical control. Tampering always degrades the performance of a system. He described the case of a machine that had a feedback device attached to measure each part and to adjust the tool location based on that measure to try to improve the repeatability of each part. It made the variation in parts much larger, because the measurements included the natural variation (capability) of the system to produce parts. The tool simply amplified that natural variation.
Tampering relates to the measurement and control of project per- formance, and the decisions to take management actions based on those measurements. This phenomenon means that responding to common cause variation as if it were special cause variation will make the system performance worse. In other words, responding to small variances by making project changes degrades project performance.
The government provides an ongoing set of examples for mistake number 2. Something undesirable happens, and they put in place a law to ensure it never happens again. We end up with thousands of pages of
regulations and laws, each applicable to some rare event or events not even applicable to the subject of the action. Mistake 2 is the essence of the growth of bureaucracy. It happens in business every bit as much as in government.
All the estimates in a project plan are uncertain. Performing each of the tasks within a project plan is a single trial of a system (the project task performance system) and is, therefore, unpredictable. However, statisti- cal techniques enable us to predict with known precision the likely results of numerous trials from a production system and to separate out the spe- cial causes of variation requiring corrective action. While knowledge of variation has been used to great profit in production operations, it has not (until now) been used to improve project performance. The PMBOK
Guide and the supporting literature we have examined fail to differenti- ate between common cause variation and special cause variation, a major oversight in the current theory.