What is Six Sigma?
I recently attended CRE exam review for the American Society of Quality and heard an engineer about her Six Sigma project. She was mentioning that she had a "Vitalaxis" presentation to give. Considering myself reasonable educated in Six Sigma (I had helped edit the last certification exam by ASQ), I asked "What is a vitalaxis?" Looking at me like I was a simpleton, she explained that they had to do with Six Sigma. Afterwards, I inferred she was saying the vital x's or critical variables and that this was considered common language at her company.
This brought to light a common question, "What is Six Sigma?" To quality professionals, it seems to be another fad like Total Quality Management. To executives, it is the methodology that enabled Jack Welch to turnaround GE. This issue of Quality Concepts will attempt to highlight a few of the points of Six Sigma so that you can at least understand some of the principles.
DMAIC
Six Sigma is a combination of problem solving methodology and quality tools. It encourages businesses to break down their operation into processes and then establish process control. The problem solving methodology is abbreviated DMAIC: Define, Measure, Analyze, Improve, and Control. In Define, the business attempts to understand the problem and reduce it to a series of metrics that can be measured. Oftentimes, the critical variables (X's) are identified as those which have the greatest impact (Y's) on the operation. In Measure and Analyze, data is collected and analyzed to determine how the critical variables impact the operation. Sometimes, the process must go back to Define to refine the problem. When the problem is understood, improvements are made and data collected to verify their effectiveness during the Improve phase. Finally, controls are implemented to ensure that all of the improvements become a permanent part of the operation in the Control phase.
All in all, it is a pretty effective problem solving scheme. Instead of half-baked guesses and shots in the dark, it uses data to justify process changes. The problem, of course, is that everyone has to agree to do it this way. One crazed engineer or technician can screw everything up by "fixing" the problem during the define or measure phases rendering all of the data questionable. Also, many people have trouble interpreting the data subjectively, so some
Of Quality Tools and Black Belts
Six Sigma uses the same quality tools that a Quality Engineer would use. Pareto charts, multi-vari charts, affinity diagrams, statistical process control, and design of experiments are commonly used. Since the discipline does not require participants to be engineers, the technical aspects of the work is often relegated to a series of Minitab¨ operations. This does have an unfortunate effect that people tend to become enamored of the software and techniques even though they may not be appropriate to the problem.
Because of the highly disciplined nature of the system, practitioners became known as Black Belts similar to karate. In the informal lexicon, a Green Belt is someone who does Six Sigma projects in addition to their regular job while Black Belts are full time Six Sigma project leaders. There is some variation in the different industries, but I like this one since it is clearly delineated. Oftentimes, Green Belts will know as much or more than Black Belts, so using knowledge as a guide is not too helpful. It is true that the typical Green Belt certification was given after two weeks of training and one project, as opposed to four weeks training and two or three projects for a Black Belt.
The Good and the Bad
The system does work, for those willing to follow the plan and apply it to the right problems. Not every problem is suitable for Six Sigma, namely those where the cause is obvious but no one wants to implement a solution. In these cases, it is a management problem that requires a different tack. It is ideal for chronic problems that never seem to go away but always come back. Some examples of good Six Sigma projects are reducing downtime, increasing capacity, reducing waste, and reducing errors.