Dynamic Estimated Time of Restoration (ETR) Calculation and Update Automation

Static ETRs create operational friction and customer distrust. Dispatchers manually guess restoration times using fragmented data, leading to missed commitments and high-volume complaint calls. A custom automation workflow eliminates this guesswork by ingesting real-time signals from crew GPS, field images analyzed for repair complexity, and parts inventory systems. This continuous calculation provides a reliable, data-driven ETR, reducing dispatcher toil and managing customer expectations during outages, directly improving SAIDI metrics and customer satisfaction scores.

Implementation integrates with utility SCADA, workforce management, and inventory systems via APIs. The orchestrator, built with a framework like LangGraph, manages state and data fusion. A key control is the configurable approval gate: high-confidence updates auto-publish, while high-impact or low-confidence ETRs route to a dispatcher dashboard. Observability tracks ETR accuracy over time, and the system is designed for incremental rollout, starting with a single feeder. This architecture turns ETR management from a reactive manual task into a closed-loop, self-improving operational asset.

This workflow automates the most critical yet manually intensive task in outage management: providing accurate, updated Estimated Times of Restoration (ETR). It eliminates the bottleneck of dispatchers manually calling crews, assessing images, and checking parts systems. The operational upside is direct: reduced customer call volume, managed public expectations, and freed dispatcher capacity to focus on higher-order grid coordination. Savings come from lower overtime labor, improved customer satisfaction scores, and faster overall restoration cycles by providing crews with precise, data-driven timelines.

Implementation integrates with the utility's OMS (e.g., Oracle Utilities OMS, Siemens Spectrum) and crew dispatch software via APIs. The core is an orchestration agent, built on a framework like LangGraph, that sequences calls to vision models for image analysis, queries the dispatch system, and checks inventory databases. Controls are essential: all ETR updates require a confidence score threshold and can be routed to a human dispatcher for approval if complexity is high or confidence is low. Observability is built in, logging each data source's contribution to the ETR calculation for audit and continuous model tuning.

This workflow automates the manual, high-pressure process of calculating and updating Estimated Times of Restoration (ETR) during outages. It ingests real-time signals from crew dispatch systems, repair complexity assessments from field images via computer vision, and parts availability from inventory databases. By synthesizing these live data streams, it continuously refines the ETR model, eliminating the need for dispatchers to manually call crews and guess repair durations. The operational upside is direct: reduced call center volume, improved customer satisfaction through accurate communication, and freed-up dispatcher capacity to manage more complex restoration logic.

Implementation requires phased integration with the OMS (like Oracle Utilities NMS, Siemens Spectrum, or GE PowerOn), crew mobile applications, and inventory APIs. The ETR Orchestrator, built on frameworks like LangGraph for deterministic agent logic, must include approval gates for major ETR extensions before public release. Observability is critical: dashboards must track ETR accuracy against actual restoration, flag data quality issues from field apps, and log all changes for audit. This creates a closed-loop, self-improving system that turns fragmented operational data into a trusted customer communication asset.

Dynamic ETR automation directly reduces customer call volume during outages and improves satisfaction by providing reliable, self-updating information. The operational upside comes from automating the synthesis of disparate, time-sensitive data streams—crew GPS status from mobile workforce management, repair complexity assessments from field agent image analysis, and parts availability from inventory systems—into a single, authoritative ETR. This replaces a manual, error-prone process where dispatchers make phone calls and guess, freeing them for higher-value fault coordination.

Implementation requires a stateful orchestrator, like LangGraph, to manage the multi-source data fusion, apply utility-specific business rules for ETR calculation, and handle exception routing. Critical controls include a confidence scoring layer to gate automatic publication, a mandatory human review loop for low-confidence or high-impact outages, and immutable audit logging of all data inputs and ETR changes for regulatory and operational post-mortems. Rollout is phased, starting with non-critical circuits, to validate data integration and user trust before full deployment.