This “30,000-foot-level Charting: Infrequent Failures” discussion addresses stability and predictability when count data occur infrequently in a process.
Predictive Analytics Models Example Infrequent Failures: 30,000-foot-level Charting
When count data occur infrequently, the time between events can be monitored over time for stability and then, if the process were stable, provide a prediction statement.
Traditionally, a c-chart methodology would be used to track count data over time to determine if special-cause events occur, which need to be addressed; however, there are issues with this approach as described in C-chart: Issues and Resolution.
To illustrate an infrequent failures analysis approach, the time between incidents data in Table 1 will be analyzed using a 30,000-foot-level charting methodology1.
Table 1: Time Between Each Incident
A 30,000-foot-level chart of these data is shown in Figure 1.
Figure 1: 30,000-foot-level Chart of Time between Incidents2
This chart indicates that our process is predictable with an estimated mean time between incidents or mean time between failure (MTBF) rate of 84 days with 80% of the incidents (i.e., 4 out of 5 times) occurring between 50.4 and 117.6 days. This value could be converted to an average annual or monthly incident rate.
Reference the article C-chart: Issues and Resolution for a more detailed explanation of the methodology summarized in this paper.
The estimated time between failures or incidents can be expected to be about the same in the future unless something changes. To improve a process’ common-cause level of performance when reported at the 30,000-foot-level, the process needs to be enhanced; e.g., through a Lean Six Sigma improvement project. This approach to improvement project creation would be a 30,000-foot-level metric pulling (using a Lean term) for an improvement project creation.
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.
A one-minute video describes issues predictive analytics models scorecard reporting and how IEE resolves the issues:
Predictive Analytics Models Example Infrequent Failures: References
- 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
- Figure created using Enterprise Performance Reporting System (EPRS) Software