Variation

A Simple Model of a Variance Stable Process

Most fairly accurate descriptions of equipment and/or process lifetimes assume that failure rates follow a three period I II III bath tub curve pattern where failures/errors decrease during the debugging or improvement time period, remain relatively constant and at their lowest levels during the normal equipment or process operating period, and increase during the wearout time period.

Mining for Lost Gold in the Transactional Environment

Transactional projects in Six Sigma often center on process chaos or, in other words, time or money being lost in long wait times, missed deadlines and expensive firefighting. The best way to restore order is via a repetitive cycle of measurement and analysis.

Remember Common Cause Variation in Fight Against Waste

Practitioners often approach forms of variation differently, with special cause variation taking priority. But it may be profitable for practitioners and company leaders to reconsider and pay closer attention to variation from common causes.

Seeking the Causes of Variation

Not all variation is created equal. Planned variation, like that in an experiment, is a process improvement strategy. Unplanned variation, however, is nearly always bad.

Understanding Process Variation

Conformance to customer CTQs can be measured in process variation and is important in the Six Sigma methodology, because the customer is always evaluating our services, products and processes to determine how well they are meeting their needs.

Variation – The Root of All Process Evil

How to evaluate and eliminate variation in your business processes.

What Is a Stable Process?

Process stability is one of the most important concepts of any quality improvement methodology. Stable processes involve achieving consistent and, ultimately, higher process yields through the application of an improvement methodology. Learn more about stability.

What You May Not Know About Adding Variances

Six Sigma instructors tell their students add variances, not standard deviations. What students may not have learned is that this additive property relies on the linearity of the equation relating the distributions together.