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Loose or Tight Monetary Policies ? The debate over whether central banks around the world must pursue loose monetary policies or tight monetary policies has become sharp in the aftermath of the global financial crisis. Loose monetary policy refers to the practice of central banks keeping interest rates low, stimulating the economy as banks would […]
Sigma or standard deviation is a statistical measure of dispersion in data. It is a measure which uses the characteristic of past data to make judgements about how the process will perform in the future. If a given set of data has normal probability distribution then the number of defects that will occur in the process over a period of time can be known depending upon the sigma level of the process.
The sigma (standard deviation) is multiplied with the numbers 1, 2, 3 etc to come up with a range. For example lets assume that the average of a data set is 10, while the sigma is 2. Hence 1 sigma will include all the data points between 10 +/-2 i.e. between 8 and 12. A 2 sigma measure would include 10+/-2(2) i.e. all data points between 6 and 14. So on and so forth.
However, it is a known fact that in a normal distribution the points tend to be around the mean. Therefore all the points will be closer to the average. Hence as one goes to farther and farther sigma levels the additional gains in terms of productivity are less.
To be a six sigma organization, the processes of an organization must be able to support this kind of efficiency sustainably. Building such robust processes requires a lot of intelligence and effort.
| Sigma Level | Defects/Million | Percentage Defects |
| 1 | 691462 | 69% |
| 2 | 308538 | 31% |
| 3 | 66807 | 6.7% |
| 4 | 6210 | 0.62% |
| 5 | 233 | 0.023% |
| 6 | 3.4 | 0.00034% |
| 7 | 0.019 | 0.0000019% |
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The Cost of Poorly Performing Processes – Need for Six Sigma
February 12, 2025
