Six Sigma is usually considered for large corporations with significant resources and a large number people to fill the role of Champions, Black Belts and Green Belts.

A small business can apply the Six Sigma methodology in their business environment and achieve similar improvement and savings.

Six Sigma drives the company’s goals toward fewer defects and higher levels of quality and customer satisfaction. To monitor the success of the company to achieve these goals, it is critical to properly measure and confirm the quality of materials, parts, and processes.

The common feature of measurements occurring during any type of product quality inspection or statistical process control is the comparison of a measurement result against a value determined by a specification or calculated process limit. This allows us to identify the conformance of the product to established requirements or the presence of the stage of statistical control for processes.

Most of the currently used metrological guidelines determine measurement accuracy, stability, repeatability, reproducibility, etc. as absolute values without regard to other parameters of a metrological system. For example, according to these guidelines, the more accurate the system is the better it is. However, the increased accuracy ultimately gives the rise to economic issues such as more expensive measurement equipment, extended inspection time, and as a result, increased costs of measurement operations. So, how can we optimize the measurement system with the goals of increasing accuracy and reducing measurement costs?

This article offers the criteria based on Type I (rejection of a good part) and Type II (acceptance of a defective part) errors occurring during the quality control. The paper shows how the criteria can connect accuracy and measurement costs related to re-work (Type I error) or warranty (Type II error).

It is possible to perform beyond the 3.4 ppm level associated with Six Sigma on a routine basis, often by many orders of magnitude. Achieving extreme levels of performance is a natural consequence of reaching a state of complete process knowledge, i.e., a state where a comprehensive understanding of all significant process input/output relationships is developed for all relevant quality characteristics of the process output. Complete process knowledge, or CPK, frequently leads to performance at “double-digit” standard deviation levels (abbreviated as ddσ or d2σ).
CPK is attained through the application of a relatively small set of improvement tools, a number of which are included in Six Sigma programs but must be employed in a slightly different fashion. To achieve sustained performance at double-digit sigma levels, one must be knowledgeable in the appropriate application of the following seven tools:

1) TOPS
2) Cost systems
3) MSA as applied to high Ppk processes
4) SPC as applied to high Ppk processes
5) Process Paths
6) DoE and Signal-to-Noise Ratio analysis
7) Multiple Response Analysis Tables

Processes optimized via this methodology will cost less and often have demonstrated the ability to run at 10, 20, or even 30σ levels when compared to original engineering specifications.