Step by Step Guide to Six Sigma Process
Here is a simple but detailed step explanation of the Six Sigma process.
Six Sigma is a averagely simple construct, but is complicated by the use of statistics. Here is a simple step by step account of the Six Sigma process that is easy for a non-technical person to follow.
Once you get a good understanding of the concept, you can begin to learn some of the tools of Six Sigma that can be used to apply the methodology in practical projects.
Be considered a process. Each process has some kind of performance goal.
Number of days it takes for a piece of mail to reach your destination (Target – 3 days)
Time needed to complete the review of a loan application (Target – 30 minutes)
Number of players badly in a manufacturing shop, etc. (Target – 3 out of 100)
It is rare that the process of meeting this target of 100% of the time. There is always some variation associated with the process. This variation will cause the process to be off the target a little. This is perfectly normal and every process is allowed a certain degree of variation in the goal. While the process performance is within the variation permitted, the process is considered good.
For example, time to make a cake is supposed to be 7 minutes, but allowing a variation of plus or minus 1 minute. This means that the time to make the cake may vary between 6 to 8 minutes, although the waiting time is 7 minutes.
Sigma value is a standardized way of indicating how often the performance of the processes that are within the allowed variation. The higher the sigma value, the better the process.
For example, a 6 sigma process has a 99.99966% chance of keeping within the limits of variation allowed.
Now, let’s get to the statistical part of the process. You can see the first significant terms
a) The theoretical goal of the process is the average of TARGET. For example, a hole diameter should be 3 feet.
b) The variation allowed for the process of the average target is tolerance. For example, the diameter of the hole above may vary plus or minus 1 foot. So the diameter could be between 2 feet and 4 feet.
c) The range of this tolerance is called the control threshold. The lowest value (in the case above, 2 feet) of this range is called the lower control limit (LCL) and superior value (4 feet in the previous case) is called the upper control limit (UCL).
Now we look at the process and collect some data on how the process is performing. For example, measure the diameter of a number of holes mentioned in the previous example.
The sigma (σ) of the process is calculated on the basis of these measurements. σ is simply the measure difference of the process, which, in simple terms, is the change we expect to see in the actual process.
The goodness of the process is determined by comparing the variation of the allowed variation process. If you have a low value for σ compared to the allowed variation is very unlikely that the process went beyond the allowed variation.
The level of σ for the process is simply the proportion of the variation permitted to process variation and indicates the capability of their process not lost outside the control limits on both sides.
i.e. σ Level = (UCL – LCL) / 2 σ
Now, you’ve noticed that the control threshold has been divided by 2 σ to calculate the sigma level. This is because it is assumed that the process average is at the center of the control threshold and the level of sigma is the ability of the process to stay on the edge on each side of the mean. On this assumption, the sigma level is calculated as Cal also,
σ level = (Mean – LCL) / σ OR (UCL – Mean) / σ
Where (mean – LCL) and (UCL – mean) gives the same amount of variation allowed (equal distance on either side of the mean)
Now, if the action has only 1 control limit (eg a process with an expected success rate of 100% and a tolerance of 10%, UCL, Average = 100% LCL = 90%), the same formula can be used to calculate the sigma level.
Sigma level tells you the probability that our process to remain in the allowed variation. For example, a 3 sigma level means that 93.31% of the time, the process would remain in the allowed range. ie if you make 1 million pieces, 66,800 pieces defects (outside the allowed variation)
Here’s a chart with the probability associated with each level of sigma.
These values have been calculated using probability functions.
Now you must have noticed the bell-shaped graphics to represent the processes. Here’s why.
If you plot all measurements during the process, there is a good probability that most measurements are close to average.
The bell-shaped curve is simply the probability of occurrence for each measurement. So there is a high probability of a measurement that are near the average and as we move toward the side of the mean (closer control limit), there is a lower probability measurements. The area outside the control limits represent the probability that the measures that are outside the control limits.
The change of the mean.
However, it is not necessary for the process means being right in the center of the control limit. This is called the change of the mean.
What this means is that the distance between the control threshold and the average is not even on both sides. So to be on the safe side, the sigma level is calculated based on the shorter side.
I.e. Sigma level = Lower of (UCL – Mean OR Mean – LCL] / σ
This difference is divided by the Sigma will give you the level of the process. Say, for processing more than 6 Sigma average of 1.5 σ has passed, now the smallest gap is 4.5 Sigma and the process now becomes a 4.5 Sigma process rather than a 6 Sigma and defects is now 3.4 ppm.
There is a notion that the process Six Sigma means 3.4 ppm. This is not true, the process of return of 6 σ 0.002 ppm. The concept came from a 3.4 ppm Motorola in the process that decided on a process called Six Sigma, if the USL – LSL / 2 σ = 6 and the average has risen from 1.5, getting real-sigma value 4.5 and the corresponding 3.4 ppm.