3 Sigma vs 6 Sigma: Understanding the Difference

3 sigma and 6 sigma are two major quality management concepts used in manufacturing and other industries. While they may sound similar, there are some key differences between these methodologies that impact how they are applied. In this article, we’ll break down the core principles of 3 sigma vs 6 sigma and when each approach is most useful.

What is 3 Sigma?

3 sigma, also referred to as three sigma, is a statistical measurement used to define quality standards. The idea is that for any process or dataset, the values will follow a normal distribution with an average and standard deviation. In a normal distribution, about 68% of values fall within one standard deviation from the mean. Expand that to three standard deviations from the mean, and it encompasses 99.73% of values.

Therefore, in three sigma quality standards, 99.73% of products or measurements will fall within an acceptable range. Any variations outside of that range are considered defects. For many common processes, achieving three sigma quality is adequate. However, for mission-critical applications like manufacturing and healthcare, three sigma leaves more room for error than is acceptable.

Key Points on 3 Sigma:

• Based on statistics and normal distribution
• 99.73% of values fall within 3 standard deviations of mean
• 0.27% fall outside the acceptable range
• 2,700 defects per million opportunities
• Adequate for many processes, but insufficient for mission-critical ones

What is 6 Sigma?

The concept of 6 sigma originated at Motorola in the 1980s and was widely adopted by other companies like GE and Toyota. It aims for even higher quality standards than 3 sigma.

6 sigma defines quality targets as six standard deviations from the mean of a process. That encompasses 99.99966% of values within specifications. It allows for far fewer defects or errors, with only 3.4 defects per million opportunities. This near-perfection is required for products and processes where quality lapses can have serious consequences.

Key Points on 6 Sigma:

• Statistical target of 6 standard deviations from the mean
• 99.99966% of values fall within specifications
• 3.4 defects per million opportunities
• Considered “near-perfection”
• Required for mission-critical products and processes

Comparing 3 Sigma vs 6 Sigma

While both are statistical quality metrics, 3 sigma and 6 sigma differ significantly in their approach and applications Here are some key comparisons between the two methodologies

• Acceptable Quality Level 3 sigma accepts 9973% as sufficient quality, while 6 sigma targets 9999966% under its tighter specifications.

• Acceptable Defect Rate: With 3 sigma, up to 2,700 defects per million are considered acceptable. For 6 sigma, only 3.4 defects per million meet quality standards.

• Process Variation 3 sigma allows for more process variation from target. 6 sigma has tighter control limits to reduce variation

• Use Cases: 3 sigma is suitable for general processes where some defects are permissible. 6 sigma is mandated for mission-critical applications.

• Implementation Complexity: 3 sigma is easier to implement but gives more modest quality gains. 6 sigma requires greater statistical rigor and process control for dramatic quality improvements.

When to Use 3 Sigma vs 6 Sigma

Understanding the strengths and limitations of each approach is key to selecting the right quality management system.

Use 3 Sigma When:

• Quality standards of 99.73% are sufficient
• Process defects will not cause major issues
• Tighter control would be too expensive or time-consuming
• Getting started with statistical quality control

Use 6 Sigma When:

• Defect rates above 3.4 per million are unacceptable
• Product or process failures would be dangerous or highly costly
• Pursuing continuous improvement to world-class quality levels
• On mission-critical products/processes where quality is paramount

Here are some examples of where 3 sigma or 6 sigma are most appropriate:

3 Sigma

• General administrative processes
• Basic manufacturing
• Non-critical services

6 Sigma

• Automotive manufacturing
• Aerospace manufacturing
• Healthcare procedures
• Banking transactions
• Computer chip fabrication

Implementing 3 Sigma vs 6 Sigma

Adopting three sigma or six sigma involves committing to data-driven quality practices. However, 6 sigma is a much more intensive undertaking.

3 Sigma Implementation

• Define quality target metrics
• Gather data on current process performance
• Calculate process variation and defects
• Identify root causes of defects
• Control process variation within 3 sigma limits

6 Sigma Implementation

• Follows structured DMAIC or DMADV framework
• Statistical analysis of capability and performance
• Tighter process controls to reduce variation
• Rigorous monitoring, measurement and analysis
• Cultural focus on quality and excellence

Achieving 6 sigma levels calls for advanced statistical skills, management commitment, and a culture of quality permeating the whole organization. But for critical applications, the investment is well justified by the substantial improvements in product and process quality.

Key Takeaways on 3 Sigma vs 6 Sigma

• 3 sigma aims for 99.73% quality, allowing 2,700 defects per million, while 6 sigma pursues 99.99966% quality with only 3.4 defects per million.

• 3 sigma provides a solid foundation for quality control, while 6 sigma takes it to the next level for mission-critical processes.

• 3 sigma is easier to implement but gives more modest gains, whereas 6 sigma requires deep commitment to achieve dramatic improvements.

• When selecting 3 sigma vs 6 sigma, weigh the acceptable quality level for the application, resources to implement quality processes, and the impact of potential defects.

• For world-class quality management, 6 sigma is the premier methodology, especially for manufacturing, healthcare, finance and technology.

Understanding when to employ 3 sigma versus 6 sigma standards allows organizations to balance quality objectives with implementation costs and practicality. By leveraging the right quality management system for each process, companies can optimize for high quality outputs while avoiding excessive investments.