Laney P’ Chart vs. a 30,000-foot-level Report

A Laney P’ Chart has advantages over a p-chart but is different and has another objective than a 30,000-foot-level report alternative.

The following dataset will be used to illustrate the differences between a traditional p-chart, Laney P’ chart, and 30,000-foot-level reporting.

 

Traditional p-chart Analysis

A traditional p-chart of the above dataset’s failure rate is

 

 

This chart indicates the process is “out of control.” With this control chart output, an organization should take corrective action to address these identified out-of-control signals.

However, there are some mathematical issues with a traditional p-chart, as described in “Issues and Resolution to p-chart Control Limits Formula False Signals

Laney P’ Chart

A Laney P’ Chart addresses the mathematical shortcomings of a traditional p-chart.

A Laney P’ chart for this dataset is:

 

Laney P' Chart

 

Unlike the previous p-chart, the Laney P’ chart shows no special-cause signals.

However, not unlike other control charts, Laney P’ Chart’s purpose is to “control” a response, i.e., promptly identify special cause issues for resolution.

A control chart’s purpose is not to first describe whether a process is stable from a high-level process-output perspective (30,000-foot-level view). And, when a process-output response is considered stable, provide a “process capability” predictive statement (in the same chart) that a CEO-to-line operator can easily understand, e.g., estimated percentage non-conformance rate.

30,000-foot-level reports address this traditional control chart reporting shortcoming, i.e., in general, not just for a Laney P chart.

There is a need for process improvement if a 30,000-foot-level report’s predictive statement is undesirable. This process-improvement consideration is not the primary objective of control charts, i.e., for all control charting, not just Laney P’ charts.

30,000-foot-level Report Alternative

A free app can create a 30,000-foot-level chart for various process-output Excel data situations, e.g., attribute and continuous data (with a specification or not). Applications of 30,000-foot-level reports include monthly profit margins reported over many years, an organization’s non-conformance rate reported monthly, on-time delivery, supplier quality, cycle times, lead times, and employee retention.

A 30,000-foot-level report for the above data set is

 

Laney P' Chart alternative - 30,000-foot-level Report Out

 

This 30,000-foot-level report is similar to a Laney P’ chart; however, there is also a prediction statement at the bottom of the report-out.

This process output response over time is stable, i.e., no puts beyond Upper Control Limit (UCL) and Lower Control Limit (LCL) lines. From this report, there is a prediction statement, i.e., estimated non-conformance rate of  about 2.1%.

If this bottom-of-the-report statement is not satisfactory, there is a need for process improvement. The success of process improvement is a staging this chart to an enhanced process-output response level.

Examples: 30,000-foot-level Reports

The books Management 2.0: Discovery of Integrated Enterprise Excellence  and Leadership System 2.0: Implementing Integrated Enterprise Excellence   include 20 example 30,000-foot-level reports for various process-output situations.

30,000-foot-level report examples from these books created with the free 30,000-foot-level creation app are:

Continuous data with no specification (Management 2.0 Figure 9.7)

 

 

This report’s prediction statement for the process output response is a mean of $100,411 with an 80% frequency of occurrence (four out of five measurements) between 90,318 and $110,504. If this futuristic statement is not desirable, there is a need for process improvement.

If there were a monthly upper limit of $110,000 for financial reasons the 30,000-foot-level report would be

 

 

From this 30,000-foot-level report, one would expect about 11% of future monthly expenses would be $110,000 or beyond.

Another example is a 30,000-foot-level report describing the impact from a process improvement project, i.e, the staging of the individuals chart and the probability plot creation of the data (with predictive statement) after the staging (i.e., Figure 6.20, Leadership System 2.0: Implementing Integrated Enterprise Excellence)

 

 

Conclusions

Laney P’ chart addresses the mathematical shortcomings of a p-chart; however, a Laney P’ chart methodology requires a statistical software package such as Minitab to execute. Leadership does not typically have access or interest in executing such software to measure a control response for a process.

30,000-foot-level reporting addresses this issue and is appropriate for the monitoring of a process output response (for virtually any response type) that can provide a predictive statement when the process output response is stable.  Also, there is a free app for creating a 30,000-foot-level response for virtually any proess-output dataset that the business may encounter; i.e., Minitab or no other statistical software is required to create the report-out.

 

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