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RCM Quality Dashboards
An RCM Dashboard shows key quality aspects of RCM analyses.
In this example we demonstrate how the RCM Quality checks described earlier in the IMS Handbook can be visualized, for example in a Dashboard as this with a number of RCM aspects shown:
Please note that more background info on creation and working with IMS Dashboards is available in the designated Handbook paragraphs:
Some elementary RCM Quality checks are described in these Handbook paragraphs, aiming to show areas of RCM data that may need investigation, follow-up, and correction:
Aspects for investigation, follow-up, and correction can be, for example:
For H (and E) criticalities it is good to check the failure effect on credibility.
For MH (and H) residual risks it is good to check the maintenance strategy effectiveness.
For X priorities it is good to check credibility and quality analyses.
For MEI classes 0-1 it is good to check the maintenance strategy effectiveness.
Incomplete analyses may need follow-up to complete.
Non-approved analyses need verification to adjust the maintenance strategy.
Low and high ETBC/ETBF ratios need verification on effectiveness and credibility.
Low and high ETBF VALUES need verification of the failure effects.
High task count analyses need verification of the failure effects and alignment of the FM definition and task application..
A new Dashboard is created by clicking [+ADD NEW] and a new Dashboard Group and a Section is created. The dashboard name will be visible in the list of dashboards:
By clicking [+ADD NEW] will create an empty Group and Section.
Next a data source selection can be made which grid data is shown, in this case data from the RCM Analyses grid. Note also queries (created in the Custom Report Grid) can be selected here.
A name and default filter can be selected, in this case an overview of Analyses Completeness and Active Flocs.
Next a Dimension can be selected from the Analyses grid, to be shown in the graph.
In this example ‘AC’ - ‘Analyses Completeness’.
At selection the data is shown, and a graph shape/layout format can be selected:
Further fine tuning of the visual appearance is enabled, like:
legends, counts, percentages, sorting and renaming of the various categories.
Renaming of the various categories (Non, FE+CA, etc) enables fine tuning of the graphs.
At Save, the first Element will be shown in this Section of the Dashboard Group.
The size and position of the Element can be further adjusted manually.
Follow up adjustments of the created Element are enabled via [ ]Edit in the Group and [ ]Edit in the Element.
At re-opening the Dashboard it might be needed to click: [ ] Configure and Arrange,
to enable [ ]Edit:
In this Group – involving Analyses data from the grid – a next element can be added via [+]Element.
This example involved an overview of the RCM criticalities:
At Save, this second Element is added to the Section.
Next, an element is added involving an overview of the Residual Risk:
Next, an element is added involving an overview of the Approved Status:
Next, elements are added involving:
Overview of MEI classes,
Overview of the ETBC/ETBF ratio classes,
Overview of ETBF values
Putting the Elements in one Section enables the Filter to apply to all the Elements.
Putting the Elements in separate Sections enables to Filter the Elements individually.
Creating a new group enables to visualize data from a different source, like Systems data:
Number of Systems per Plant:
Number of analyses per System:
Next RCM data is shown in a Dashboard regarding RCM coverage - in a new Group based on Flocs:
Note that from the Floc grid the columns ‘Location Type’ and ‘#FMs’ are used:
In this metric the number of FM’s per tag is shown in a pivot table, per location type. Such a table shows the depth of analyses.
Note: for a Pivot table 2 columns are needed! Here: #FMs and Location Type
By creating a new Dashboard Group, a similar table can be build based on Equipment groups:
Through ‘Configure and Arrange’ the Dashboard Groups, Sections and Elements can be arranged, and suitable data can be shown together, like in this example data on Floc and Eq level: