Six Sigma is a scientific term that some smart guys figured out how to coin into a new important buzzword in the business lexicon. The foundations for Six Sigma can be traced back to the early 20th century and the first efforts to focus on quality as a key competitive factor. It was not until the formation of the Japan Union of Scientists and Engineers (JUSE) by Gen. Douglas MacArthur at the end of World War II that the modern concepts of Total Quality Management took shape in a cohesive way.
The scientific term Six Sigma is derived from the values found in a normally distributed bell-shaped curve of a population of data. Without giving a lesson in statistics here, let’s use a simple analogy to understand what this means.
Understanding the Concept of Six Sigma
In the graphic on this page, we can see what this normally shaped curve looks like. To create a hypothetical scenario that puts this into perspective, let’s pretend we were to capture the height of each male living in the U.S. We add up all the measurements and calculate the average value of 5’ 10” tall — the mean value. If we calculate one standard deviation upward and downward from there, about 68% of the population of U.S. males would be between 5’ 6" and 6’ 2” in height.
Measuring two standard deviations left and right, we would take in about 95% of the U.S. male population — say 5’ 1’ and 6’ 9”. Measuring three standard deviations left and right takes in 99.997% of the population, or all but three men per million who would be less than 3’ 6” or more than 7’ 4”.
So, Why Do We Care?
The reason this is important is that once we gather a population of data about a business process we can then view the data graphically to understand how it is behaving and make some important observations. An example would be the diameter of a hole being drilled in a piece of steel. After collecting measurements of hundreds of holes and recording the data in a curve format, we can make observations. Say the desired hole is 1” in diameter and actual measurements range between 0.95” and 1.05”. If, after plotting the values, we see a normal bell-shaped curve, we know the process is behaving as expected. We might observe that within one standard deviation up and down that 68% of the holes were between 0.98” and 1.02”. All is well — so far.
If the specification requires the hole to be 1” in diameter plus or minus 0.02”, we are in big trouble. Why? Because only 68% of the parts met the specification and we must now sort out and throw away the 32% that are out of tolerance. We know that we must reduce the variation in this process. A solution might be rebuilding or replacing the drilling machine to drill tighter holes or using sharper drill bits.
A Simple Example
If there are two fast-food restaurants with acceptable food alternatives and quality near your work and the average (mean) wait time to get through the drive-through lanes is about five minutes for each store, over time you observe that there is a difference in variation between the two. Restaurant A’s wait time can be between 3-15 minutes and B is 3-10 minutes. If you are in a hurry today – which restaurant will be getting your business? Which of these two restaurants has a demonstrated competitive advantage — and probably better loyalty and profits?
Understanding variation is our enemy, finding sources of variation that our customers are concerned about and relentlessly eliminating them is the essence of Six Sigma thinking. Congratulations — you now know more about Six Sigma than 99% of HR executives in the world.