Predictive Analytics Models Example Attribute Data: 30,000-foot-level Charting

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.

This “30,000-foot-level Charting: Attribute Data” discussion addresses stability and predictability of an attribute-process output response over time.

Predictive Analytics Models Example Attribute Data: 30,000-foot-level Charting

Attribute, pass/fail proportion data, can be monitored over time for stability and then, when a process is stable, provide a prediction statement.

Consider that the attribute proportion data in Table 1 were collected using an infrequent subgrouping/sampling plan, which is consistent with a 30,000-foot-level charting methodology1

Traditionally a p-chart methodology would be used to track this type of data over time; however, there are issues with this approach as described in P-charts: Issues and Resolution.

 

Predictive Analytics Models Example Attribute Data: 30,000-foot-level Charting: Data Set

Table 1: Time-Series Data from Process

 

The 30,000-foot-level chart, as shown in Figure 1, indicates that the process is stable. When a process has a recent region of stability, it can also be said to be predictable. When this occurs, we can use historical data to make a statement about what we might expect in the future, assuming things stay the same; e.g., the center line of the chart if no transformations are needed to create the 30,000-foot-level chart, and the subgroup sizes are approximately the same.

 

Predictive Analytics Models Example Attribute Data: 30,000-foot-level Charting Output

Figure 2: 30,000-foot-level Chart of Non-conformance Rate2

 

The process capability/performance metric for this process can be said to have a non-compliance rate about 0.021, which is noted at the bottom of the chart. That is, since the process is in control/predictable, it is estimated that the future non-conformance rate will be about 0.021, unless a significant change is made to the process or something else happens that either positively or negatively affects the overall response. This situation also implies that Band-Aid or firefighting efforts can waste resources when fundamental business process improvements are really what are needed.

If improvement is needed for this 30,000-foot-level metric, a Pareto chart of defect reasons can give insight to where improvement efforts should focus. The most frequent defect type could be the focus of a new Lean Six Sigma project. For this Lean Six Sigma implementation strategy, one could say common-cause measurement improvement needs are pulling for the creation of a Lean Six Sigma project.

Reference the article P-charts: Issues and Resolution for a more detailed explanation of the methodology summarized in this paper.

 

Predictive Analytics Models Example Attribute Data: 30,000-foot-level Charting Applications

The described 30,000-foot-level charting technique has many applications, as described in 30,000-foot-level Performance Reporting Applications.

IEE Predictive Analytics Models addresses traditional business scorecard reporting and improvement issues that are described in a 1-minute video:

 

predictive analytics models example attribute data video

 

Predictive Analytics Models Example Attribute Data: References

  1. Forrest W. Breyfogle III, Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Bridgeway Books/Citius Publishing, 2008
  2. Figure created using Enterprise Performance Reporting System (EPRS) Software

 

Contact Us to set up a time to discuss with Forrest Breyfogle how your organization might gain much from an Integrated Enterprise Excellence (IEE) predictive analytics model approach for attribute and other types of data.

Enhanced Attribute Data Control Chart with Process Capability Statement

Enhanced attribute data control chart reporting is provided through Integrated Enterprise Excellence (IEE)  30,000-foot-level report-outs. This form of process output tracking  includes (in one chart) a high-level process stability assessment with a predictive capability statement when the process is stability.