Work order auto-population

When you create a work order from IBM® Maximo® Renewables, key fields such as asset name, description, and long description are automatically populated based on the originating feature and context.

To reduce manual entry and improve data consistency, IBM Maximo Renewables auto-populates work order fields when you initiate a work order from an analytics observation, event, or alert. The fields that are populated depend on which feature you use to create the work order.
Note: To fill work orders automatically, the assets must be linked. for more information, see Adding assets

All auto-populated fields can be changed if needed.

Auto-populated fields by feature

The following table shows which asset, description, and long description are populated for each feature.

Work orders initiated Asset Description Long description
Tahoe > Project level pages Project Analytics Observation – {Tab Name} Observation Period – {Start date and end date from calendar}
Tahoe > Individual inverter pages Inverter Analytics Observation – {Tab Name} Observation Period – {Start date and end date from calendar}
Tahoe > Loss waterfall > Downtime loss overview > Downtime and alarms correlation Inverter Analytics Observation – Downtime and Alarms Correlation Observation Date – {Date on which the downtime is observed}; Downtime Loss
Tahoe > Loss waterfall > Late start loss overview > Inverter late start and alarms correlation Inverter Analytics Observation – Inverter late start and Alarms Correlation Observation Date, Late Start Loss, Duration, Expected Start Time, Actual Start Time
Tahoe > Loss waterfall > Tracker loss > Tracker loss time series Tracker Analytics Observation – Tracker Loss Observation Date – {Date on which the loss is observed}; Tracker Loss; Extreme Weather Loss
Tahoe > Loss waterfall > Cleaning loss overview Inverter Analytics Observation – Cleaning Loss Observation Date – {Date on which the loss is observed}; Cleaning Loss
Tahoe > Loss waterfall > Shadow loss > Irradiance vs active power Inverter Analytics Observation – Shadow Loss Observation Period – {Date on which the downtime is observed}; Shadow Loss
Tahoe > Loss waterfall > Clipping loss > Irradiance vs active power Inverter Analytics Observation – Clipping Loss Observation Date – {Date on which the loss is observed}; Clipping Loss
Tahoe > Data quality > R-square heatmap > Regression graph Inverter Analytics Observation – Data Quality Observation Date – {Date on which data is erratic}; Data Quality Class
Tahoe > Data quality > Daily generation comparison heatmap > Time series generation Inverter Analytics Observation – Data Quality Observation Date – {Date of the chart}; Daily Generation; Generation from Active Power; Absolute Difference
Recommendation engine {Asset Name} {Category} {Description} – {Loss Production}; {Recommendation}
Events {Asset Name} {Description} Event name, type, severity, raised time, resolved time, duration
Custom alerts {Asset Name} {Message} Condition name, alert severity, raised time, resolved time, duration
Turbine prognosis {Asset Name} Turbine Prognosis Observation Customer name; Observation Period – {Start date and end date from calendar}
String analysis Select manually String Analysis Observation Observation Period – {Start date and end date from calendar}
Plant performance Project Plant Analysis Observation Cluster name; Observation Period – {Start date and end date from calendar}

Events and custom alerts

For work orders created from events or custom alerts, you choose whether fields are auto-populated based on predefined messages or manually fill the details.