The standard deviation in Six Sigma is not a fixed number; it is a measure of the variation or dispersion of data around the process mean. Instead, the core idea of Six Sigma revolves around the number of standard deviations that fit within the set process boundaries or specifications. The reference states: "The number of standard deviations that can fit within the boundaries set by your process represent Six Sigma." Therefore, a Six Sigma process aims to fit a large number of standard deviations within these specifications, reducing defects and process variation.
Understanding Standard Deviation in Six Sigma
How It Works
-
The standard deviation (often represented by the Greek letter σ) quantifies the spread of data points in a dataset. A higher standard deviation indicates a greater dispersion of values, while a lower one means the data points are clustered closer to the mean.
- Example: Imagine you are measuring the length of screws produced in a manufacturing process. If the standard deviation is small, the screws will have lengths that are very close to the average. If the standard deviation is larger, there will be a larger difference in the screw lengths from the average length.
-
In Six Sigma, the goal is to minimize the standard deviation as this leads to consistent process outcomes.
The Six Sigma Goal
-
The core goal of Six Sigma, as the name suggests, is to create processes where, in the short term, the process mean is at least six standard deviations away from both the upper and lower specification limits.
-
However, because processes can shift over the long term, a shift of 1.5 standard deviations is built-in. This means a long-term Six Sigma process is achieved when 4.5 standard deviations fit within the process boundaries.
-
This approach minimizes defects, which means that only 3.4 defects per million opportunities are acceptable.
-
The reference supports this, noting: "If you can fit 4.5 standard deviations within your process specifications then you have obtained a Six Sigma process for a long term scale."
Why Standard Deviation Is Important
- Process Control: Monitoring the standard deviation helps in maintaining process control and identifying potential problems.
- Quality Assurance: A lower standard deviation translates to higher product and process quality.
- Waste Reduction: By controlling variation, Six Sigma processes can significantly reduce waste.
Example
Let’s say a manufacturing company produces a product, and the desired length of the product is 100mm, with acceptable variation being between 99mm and 101mm.
- If the process standard deviation is 0.5mm, approximately 4 standard deviations can fit on each side of the desired length. If we allow a 1.5 standard deviation process shift on the long term, we would still be within 4.5 standard deviations from both limits, making this a Six Sigma process.
- If, instead, the standard deviation is 1mm, only 2 standard deviations can fit on each side of the desired length. Allowing for process shifts would cause defective product, meaning it's not a Six Sigma process.
| Scenario | Standard Deviation (mm) | Standard Deviations That Fit | Six Sigma? |
|---|---|---|---|
| 1 | 0.5 | 4 (short term), 4.5 (long term) | Yes |
| 2 | 1 | 2 | No |
Key Takeaways
- Standard deviation is a key measure in Six Sigma.
- The lower the standard deviation, the better the process performance.
- A Six Sigma process is defined by fitting approximately 4.5 standard deviations within the process specifications on the long-term scale.
