Six Sigma is a rigorous, focused and highly effective implementation of proven quality principles and techniques. Incorporating elements from the work of many quality pioneers, Six Sigma aims for virtually error free business performance.
Sigma, (σ), is a letter in Greek alphabet used by statisticians to measure the variability in any process. A company's performance is measured by the sigma level of their business processes. Traditionally companies accepted three or four sigma performance levels as the norm, despite the fact that these processes created between 6,200 and 67000 problems per million opportunities! The Six Sigma standard of 3.4 problems per million opportunities is a response to the increasing expectations of customers and the increased complexity of modern products and processes.
|Fig. 1 Six Sigma DMAIC|
Six Sigma's magic is not in statistical or high tech razzle-dazzle. Six Sigma relies on tried and true methods that have been around for decades. In fact, Six Sigma discards a great deal of the complexity that characterized Total Quality Management (TQM). Six Sigma takes a handful of proven methods and trains a small cadre of in-house technical leaders, known as Six Sigma Black Belts, to a high levels of proficiency in the application of these techniques. To be sure, some of the methods Black Belts use are highly advanced, including up-to-date computer technology. But the tools are applied within a simple performance improvement model known as Define-Measure-Analyze-Improve-Control.
This is know as DMAIC and described as
D-Define the goals of the improvement activity.
M-Measure the exiting system
A-Analyze the system to identify ways to eliminate the gap between the current performance of the system or process and the desired goal
I-Improve the system
C-Control the new system
Why Six Sigma?
It would be a mistake to think that Six Sigma is about quality in the traditional sense. Quality, defined traditionally as conformance to internal requirements, has little to do with Six Sigma. Six Sigma is about helping the organization make more money by improving customer value and efficiency. To link this objective of Six Sigma with quality requires a new definition of quality;
"Quality is the value addition through a productive endeavor."
Quality comes in two flavors:
- Potential quality: It is known as the maximum possible value added per unit of input
- Actual Quality: It is the current value added per unit of input.
|Fig. 2 COPQ Vs Sigma Level|
The difference between the potential and actual quality is waste. Six Sigma focuses on improving quality (i.e. reducing waste) by helping organizations produce products and services better, faster, and cheaper. There is a direct correspondence between quality levels and "sigma levels" of performance.
For example; a process operating at Six Sigma will fail to meet requirements about 3 times per million transactions. The typical company operates at roughly four sigma, which means they produce roughly 6,210 failures per million transactions. Six Sigma focuses on customer requirements, defect prevention, cycle time reduction, and cost savings. Thus the benefits from Six Sigma go straight to the bottom line. Unlike mindless cost-cutting programs, Six Sigma identifies and eliminates costs which provide no value to customers and waste costs.
For non-Six Sigma companies, these costs are often extremely high. Companies operating at 3 or 4
sigma typically spend between 25 and 40 percent of their revenues fixing problems. This is known as the cost of poor quality. Companies operating at Six Sigma typically spend less than 5 percent of their revenues to fix problems. Fig. 2 shows the cost of poor quality vs sigma level.The dollar cost of this gap can be huge. General Electric estimated that the gap between three and four sigma and Six Sigma was costing them between $8 billion and $12 billion per year.
|Fig. 3 Error Rate Vs Sigma Level|
One reason why costs are directly related to sigma levels is very simple: Sigma levels are a measure of error rates, and it costs money to correct errors. Fig.3 shows the relationship between errors and sigma levels. Note that the error rate drops exponentially as the sigma level goes up, and that this correlates well to the empirical cost data shown in fig.2 Also note that the errors are shown as errors per million opportunities, not as percentages. This is another convention introduced by Six Sigma. In the past we could tolerate percentage error rates, today we cannot.