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.
A enhanced control chart that includes a process capability statement (that is easy to understand) is provided in a 30,000-foot-level report-out. Organizational 30,000-foot-level metrics can be integrated with their processes via a value chain in the Integrated Enterprise Excellence (IEE) Business Management System.
Enhanced Control Chart that Includes Process Capability in One Chart Read More »
A major issue with the xbar and r chart formula is that if a process has between subgroup variability that is common cause false, special cause signals can often occur. This is an important concept that everyone who uses xbar and r charts should understand but typically don’t. A 30,000-foot-level metric tracking approach not only
Issues and Resolution to xbar and r chart Formula Problems Read More »
Traditional p chart control limits formula calculations can lead to firefighting common cause variation as though it were special cause. This “false signal” issue can occur when common-cause process variation occurs between subgroups.
Issues and Resolution to p chart Control Limits Formula False Signals Read More »
An individuals chart that can provide a process capability statement too is a 30,000-foot-level metric report-out. At the enterprise level, 30,000-foot-level report-outs can be used in an Integrated Enterprise Excellence (IEE) Business Management System.
Individuals Chart Report that can also Provide a Process Capability Statement Read More »
A probability plot can be used to quantify process performance and process capability through a 30,000-foot-level report-out. In one chart, a 30,000-foot-level report can include a process stability assessment through an individuals chart(s) and a probability plot to make a predictive statement for process performance and process capability.
Quantifying process improvement impact using a 30,000-foot-level chart is very beneficial for a Lean Six Sigma project or Kaizen event deployments. Quantifying Process Improvement and 30,000-foot-level Predictability Statements Note: Content of this webpage is from Chapters 12 and 13 of Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and
A major issue with the c chart formula is that if a process has between subgroup variability that is common cause false special cause signals can often occur. This is an important concept that everyone who uses a c chart understand. A 30,000-foot-level metric tracking approach not only gets around this problem but also provides
Issues and Resolution to C Chart Formula Problems Read More »
Process capability problems and solutions to those issues is very important for an organization. Another way to state this need is resolving the process capability index issues of Cp, Cpk, Pp, and Ppk is essential so that organizations will take appropriate actions from their process capability reporting. The solution to this problem is easily
Transforming individuals control chart data is an important consideration to avoid common cause variability appearing as special cause events. The transformation of data (for situations that make physical sense) is easily accomplished in 30,000-foot-level tracking metric report-outs, which also can provide a predictive process capability statement — in one chart.
Provided is a video showing the creation of metrics that lead to the best behaviors. This video is an edit of Forrest Breyfogle’s February 9, 2023, presentation at an ASQ Phoenix, AZ, section meeting.
