In February’s Inbox, a letter observed that in the 2010 QP Salary Survey, the mean salary increased while the median was stagnant. The letter writer said "anyone" could conclude this meant "the rich got richer." That conclusion is not supported by the data.
The data do not compare individuals from one year to the next, only groups in which the individuals may have changed. Some members of the previous survey may have retired or been laid off, while new members joined the group or were promoted.
Even if nobody’s employment status changed, there is no control over who participated in the survey, other than them being ASQ members. Perhaps some people from the previous year were busy or otherwise did not participate in the new survey.
An alternative conclusion could be that there is more financial opportunity in the quality field than previously existed, with no losses at the lower end. Due to the reasons cited above, that conclusion also is not supported by the data but is just as valid as the conclusion that "the rich got richer."
The article on action learning ("Time for Action," January 2011) raised some interesting points. The focus on problem definition and knowledge sharing are good features of this technique, and I will see how we can integrate them into our workflow.
A past employer had something similar to action learning, informally known as "do it now" (DIN). My experience with DIN and Six Sigma at this employer convinced me of the inherent advantages of Six Sigma.
It became clear that for many problems, the expert team members had less-accurate mental models of processes and technologies than they believed. As a result, DIN suffered from a great deal of churn in which the same—or similar—problems were "solved" repeatedly.
To management, DIN had the clear advantage of "solving" problems faster than Six Sigma, but we showed that Six Sigma repeatedly solved problems correctly the first time and saved money in the long term.
In Six Sigma, activities in the measure and analyze phases refine and clarify the problem description, and verify that description and the effectiveness of proposed solutions. As a result, money and time are not spent implementing ineffective solutions.
Meanwhile, DIN, lacking the measure and analyze activities, wasted time and money. As a decision framework, DIN projects ended with a higher probability of spending money without a return on investment.
In either case, spending time clearly defining the problem and putting together the right team contributes greatly to the probability of success, and all up-front activities return a benefit, regardless of the continuous improvement method used.