By: Ted Hoiberg
A common business objective related to EAM asset management is to provide a visual reference that represents the change rate of MTBF data over time. This objective goal leads to the question of the relevance of Control Charts for MTBF data, why would you use them? What do they do to meet the objective? How would I use and create them?
The main reason why you would use Control Charts is to provide a understood degree of MTBF process visibility and to also provide a means to monitor, control and improve process performance over time by studying MTBF mean variation and its sources.
What Control Charts can do for the MTBF data is as follows:
- Focus attention on detecting and monitoring MTBF mean process variation over time.
- Distinguish special from common causes of variation as a guide to management action.
- Serves as a tool for ongoing control of the process and the associated executive KPI.
- Provides a common framework for discussing the ongoing process performance.
- Helps improve the MTBF process with performance monitoring and predictability.
- Meets a business objective key result and sets up the foundational KPI for measuring future success of RCM programs.
Here’s how SPC run charts can specifically aid in interpreting MTBF data:
- Identification of Trends: SPC run charts help in identifying trends or patterns in MTBF data over time. Trends may indicate improvements or deteriorations in the reliability of a system or process. For MTBF data, a decreasing trend could suggest declining reliability, while an increasing trend may indicate improving reliability.
- Detection of Outliers: SPC run charts can highlight outliers or data points that fall outside the expected range. Outliers may represent exceptional events or anomalies that could significantly impact MTBF. Identifying and investigating these outliers can provide insights into the factors affecting reliability.
- Understanding Process Stability: Run charts help assess the stability of a process. A stable process is essential for reliable MTBF measurements. If the run chart shows a consistent and predictable pattern over time, it suggests that the process is in control. Sudden shifts or irregularities may indicate changes in the system that need attention.
- Setting Control Limits: SPC involves setting control limits on the run chart, typically at three standard deviations from the mean. Control limits help distinguish between common cause variation (inherent to the process) and special cause variation (resulting from specific events). This is crucial in understanding whether changes in MTBF are part of normal variation or require intervention.
- Continuous Monitoring: SPC run charts provide a tool for continuous monitoring of MTBF. Regularly updating the chart with new data points allows for ongoing analysis and adjustment. This continuous monitoring helps in maintaining and improving the reliability of a system over the long term.
- Facilitating Communication: SPC run charts provide a visual representation that is easy to interpret, making it a valuable communication tool. Stakeholders can quickly understand the performance of a system or process, facilitating discussions and decision-making related to reliability improvements. Here is the simplified X Bar chart creation process that is easily replicated in Excel using the chart wizard.
In EAM, you should be able to query Work Order data segregated by Asset Class related to the time between failures. You can monitor the average time between failures over time using an X-bar chart. Here’s the high-level steps for how you can create an X-bar chart for MTBF data:
- MTBF Data Collection and Correlation of past planned work for the Asset
- Collect EAM Work Order data on the time between failures for your asset group.
- Define Analysis Subgroups:
- Group the MTBF data into analysis subgroups that is a common representation of all the various CLASS data in the Asset Hierarchy
- For example, a typical hierarchy with 25 Classes is modeled into five functional subgroups.
- Calculate Subgroup Means:
- For each subgroup, calculate the mean baseline of the time between failures
- Construct the X-bar Chart:
- Plot the subgroup means on the X-bar charts over time. The X-axis represents time, and the Y-axis represents the calculated means.
- Control Limits:
- Add control limits to the charts based on statistical analysis. Control limits help identify when the process is exhibiting variation beyond what is expected.
- Monitoring:
- Regularly update the X-bar charts with new data to monitor changes in the average time between failures. Look for patterns, trends, or points that fall outside the control limits.
A caveat to be aware of in any statistical analysis, is that an X-bar chart can provide insight into the stability of the average time between failures. It’s important to note that X-bar charts assume a normal distribution of data.
In summary, SPC run charts provide a dynamic and visual approach to monitoring and interpreting EAM generated MTBF data. They assist in identifying trends, detecting outliers, assessing process stability, setting control limits, continuous monitoring, and facilitating communication, all of which contribute to effective reliability management.
In a future release, SPC control chart usage is on the Radar screen and 21Tech is ready to help.