Statistical Start-Up / Design for Six Sigma

The purpose of a statistical start-up (sometimes called "Design for Six Sigma") is to design a new process or product, or redesign an existing one, so that it runs in a state of statistical control with all of the related end-of-line characteristics running with minimal variability around a customer-defined target. (from Luftig, 1992)

There are two papers available that discuss Statistical Start-Up. Click Luftig or Ouellette to read these papers.

Traditionally, it has been considered almost inevitable that a new process, product, or service would have to go through a phase of "working out the bugs" once it had been put into production. Too frequently, businesses have attempted to do this process tuning once the process or product is in normal production. The cost of this approach can be staggering.

This methodology can be used for new or existing production units, products, or services. By using a statistically-based strategy to achieve control and minimal variability in the output, the process minimizes risk and maximizes profit. Compare this to advanced problem-solving, which identifies and eliminates source causes for a problem or gap in an existing product or service. When you are developing a new process or product, you have many internal and external criteria that you need to meet, where advanced problem-solving is usually only looking to improve/eliminate one characteristic or non-conformity.

At ROI, we use the D²C² methodology of product, process, or service design. How the individual steps are applied vary widely by application and industry, but in general, they are:

Benefits

The benefits of statistical start-ups of new and existing equipment, products, and services are:

• The new equipment will be operating in a state of statistical control and at the maximum level of capability possible (or at high process performance capabilities) for those product and performance variables you designated in the beginning.
• The new equipment will be operating at the minimum cost profile available given the parameters defined by the process and management goals.
• A complete quality operating system that is related to the critical product and process variables including:
  • Master and process block QFD matrices
  • Design, process, and equipment FMEAs
  • Product control plans and appropriate control systems
  • Process and measurement control plans and control systems
  • Maintenance control plans and control systems
  • Reaction plans for all critical product and process variables
  • SOPs and a workforce that has been trained to use them