Analytics (with PAI) > Forecast Intelligence > Region MTBF > Region MTBF (Technical Specification)
Region MTBF (Technical Specification)
These are the mini-dashboards and Analytical Objects that are used to populate the Region MTBF dashboard.
Mini-Dashboard
Analytical Object
Causal Data By Region
These are the mini-dashboards and their associated Query Objects that are used to populate the Region MTBF dashboard:
Mini-Dashboard
Query Object
RMSE
Part Summary
RMSE
The data used to generate this mini-dashboard is based on the following:
Article
Description
Analytical Object
N/A
Query Object
QO_PAI_ML_RMTBF_BiasValue
Data Source
PAI Machine Learning
Cab File
RegionMeanTimeBetweenFailure.cab
Parameters
N/A
Causal Data By Region
The data used to generate this mini-dashboard is based on the following:
Analytical Object
AO_PAI_ML_RMTBF_Summary
Query Object
QO__PAI_ML_RMTBF_Summary
Data Source
PAI Machine Learning
Cab File
RegionMeanTimeBetweenFailure.cab
Parameters
Name
Description
Default
PO_PAI_ML_RMTBF_ErrorThreshold
The percentage threshold for ML vs Servigistics comparison on the dashboard.
For example, if the ML result is 10% better than the Servigistics result then ML the value is used. Otherwise, the Servigistics value is used.
10
Predecessor Jobs
None
Build Type
The following build types are supported:
• Full
Build Frequency
None
• #Parts
• Region Wise Count
Measure
Mathematical Function
# Parts
Total part count
Effective Part Population
Sum of effective part population value across different regions
Regionname
Count of distinct regions
Effective Causal
Sum of effective causal value across different regions
Part Summary
The data used to generate this mini-dashboard is based on the following:
Article
Description
Analytical Object
N/A
Query Object
QO_PAI_ML_RMTBF_PredictionsSummary
Data Source
PAI Machine Learning
Cab File
RegionMeanTimeBetweenFailure.cab
Parameters
Name
Description
Default
PO_PAI_ML_RMTBF_ErrorThreshold
The percentage threshold for ML vs Servigistics comparison on the dashboard.
For example, if the ML result is 10% better than the Servigistics result then ML the value is used. Otherwise, the Servigistics value is used.
10
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