Introduction to Six Sigma

Pioneered at Motorola in the mid-1980s, Six Sigma was initially targeted to quantify the defects occurred during manufacturing processes, and to reduce those defects to a very small level. Motorola claimed to have saved several million dollars. Another very popular success was at GE. Six Sigma contributed over US $ 300 million to GE’s 1997 operating income.

Originally, Six Sigma was defined as a metric for measuring defects and improving quality; and a methodology to reduce defect levels below 3.4 Defects Per (one) Million Opportunities (DPMO). Six Sigma is a registered service mark and trademark of Motorola, Inc who has reported over US$17 billion savings from Six Sigma to date.

Six Sigma has now grown beyond defect control. It can be defined as a methodology to manage process variations that cause defects, defined as unacceptable deviation from the mean or target; and to systematically work towards managing variation to eliminate those defects. The objective of Six Sigma is to deliver world-class performance, reliability, and value to the end customer.

It is important to recall that every customer always values consistent and predicable services and/or products with near zero defects. Therefore they experience the variation and not the mean. Mean is their expectation or our target.

If we can measure process variations that cause defects i.e. unacceptable deviation from the mean or target, we can work towards systematically managing the variation to eliminate defects. It is implemented via two potential scenarios - (a) there is already an existing process(s) that is working “reasonably” well and (b) there is no process at all or the process is considered to be poor.

Scenario (a) focuses on significant process improvements and requires use of DMAIC:

* Define process goals in terms of key critical parameters (i.e. critical to quality or critical to production) on the basis of customer requirements or Voice Of Customer (VOC)
* Measure the current process performance in context of goals
* Analyze the current scenario in terms of causes of variations and defects
* Improve the process by systematically reducing variation and eliminating defects
* Control future performance of the process