Process metrics need to lead to the most appropriate behaviors. Processes have variability and may or may not have specifications.
Performance measurements for processes need to provide direction to the most appropriate behaviors considering both process variability and any specification that may exist. The output of processes can have both common-cause variability and special-cause variability.
In process metric reporting, typical process variability is separated from unusual events or trends. Traditionally this separation is make using statistical process control (SPC) charts such as x-bar and R charts and p-charts. How a process is performing for an in-control process relative to specifications traditionally involves techniques such as process capability indices.
However, traditional control charting and process capability reporting have mathematical issues. An Integrated Enterprise Excellence (IEE) 30,000-foot-level reporting format addresses these issues. IEE 30,000-foot-level reporting provides both a process stability assessment and predictive statement for stable processes in one chart.
The importance of an XmR control charts and data normality consideration is that if data are not normally distributed false special-cause signals may occur in a control chart without a data transformation.
XmR Control Charts and Data Normality Read More »
This Six Sigma metrics example will use a golf scorecard analogy in the provided two articles to illustrate the confusion and resolution with traditional Six Sigma measurements reporting.
Six Sigma Metrics Example: Using Golf Scores to Illustrate Confusion and Resolution Read More »
Enhancements to a process capability analysis Six Sigma approach provide much organizational benefit not only for individual lean Six Sigma improvement projects but business performance metric reporting as well.
Enhancements to Process Capability Analysis Six Sigma Approach Read More »
