Six Sigma is a quality management methodology aiming for near-perfection. A process operating at a “six sigma” level has a yield of 99.9997%, meaning it produces only 3.4 Defects Per Million Opportunities (DPMO). This standard corresponds to a process where the nearest specification limit is six standard deviations away from the process mean, accounting for a 1.5 sigma shift in the mean over time (refer to the specific details on the debated 1.5 sigma shift as an empirical observation).
Six Sigma Quality Standard (3.4 DPMO)
- Bill Smith
- Mikel Harry
The core idea of Six Sigma is that a process should be so well-controlled that its output consistently meets customer requirements. Statistically, this is defined by the process capability. A process is considered capable at a six sigma level if the distance from the process mean to the nearest specification limit is at least six times the process standard deviation (\(\sigma\)).
However, the widely cited 3.4 DPMO figure is not derived from a pure six sigma distribution. It incorporates an empirical observation that process means can drift over time. Motorola engineers observed a long-term drift of about 1.5 sigma. Therefore, the calculation for 3.4 DPMO is based on the probability of a defect when the specification limit is \(6\sigma – 1.5\sigma = 4.5\sigma\) away from the mean. This 1.5 sigma shift is a crucial, and sometimes controversial, component of the Six Sigma methodology, distinguishing it from a purely theoretical statistical process control model. It makes the standard more attainable in real-world applications where perfect process centering is difficult to maintain over long periods.
Important note: while this target is a very “near perfection” target, one should not forget that in industries where safety is crucial AND that produces products or services with high volumes, the defect (DPMO to improve) CAN arrive, thus needing potential combined and counter mesures to lower even more or suppress completely the consequences.
Type
Disruption
Usage
Precursors
- Statistical Process Control (SPC) by Walter A. Shewhart
- Total Quality Management (TQM) principles
- the concept of the normal distribution by Carl Friedrich Gauss
- process capability studies
- quality circles movement in Japan
Applications
- manufacturing process control in automotive and aerospace
- service quality improvement in finance and healthcare
- supply chain optimization
- software development quality assurance
Patents:
Potential Innovations Ideas
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