Organizations often use a performance metrics system that only provides point to point comparisons. For example, the currently monthly profitability of a business might be compared to last month. Any movement that is not desirable often leads to a search for what happened.
This what-happened investigation can result in the treatment of common cause variability as though it were special cause. Wasteful firefighting often is a result of traditional metric report-outs and nothing positive happens relative to making a metric improve over time.
A 30,000-foot-level predictive reporting methodology addresses this traditional metric reporting format shortcoming. With 30,000-foot-level metric reporting, a predictive statement assessment is made. If a process output performance is stable and the futuristic response is undesirable, this metric desirable shortcoming pulls for the creation of a process improvement effort that enhances the measurement’s response.
Enterprise Performance Reporting System (EPRS) software provides a means to easily create 30,000-foot-level report outs. A statistical shift in the time-series portion of a 30,000-foot-level metric is an indicator that the process output changed, either to the betterment or degradation. When this happens a new predictive process statement can then be made using this software.
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 […]
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This predictive analytics models example attribute data illustration shows the value of 30,000-foot-level reporting in the Integrated Enterprise Excellence (IEE) Business Management System.
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This predictive analytics models example non-normal data illustrates the benefits of 30,000-foot-level performance reporting in an Integrated Enterprise Excellence (IEE) Business Management System.
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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.
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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.
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A probability plot can be used to quantify process performance 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. This statistical foundation supports the broader discipline of predictive performance metrics, where organizations
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
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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
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This predictive analytics models example multiple samples in subgroup illustration will demonstrate the value of 30,000-foot-level reporting which is provided in the Integrated Enterprise Excellence (IEE) Business Management System.
This predictive analytics models example infrequent failures describe benefits of 30,000-foot-level reporting in an Integrated Enterprise Excellence (IEE) Business Management System.
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