ETR (Estimated Time to restore) AI/ML Model

Capgemini's Estimated Time to Restore (ETR) AI Solution is an Azure-based offering designed for energy and utility organizations to improve outage management, restoration forecasting, and customer experience.

Key capabilities: • Predicts Estimated Time to Restore (ETR) during power outages using advanced AI and machine learning models. • Analyzes more than 40 data variables, including weather conditions, geospatial information, network data, asset information, and outage characteristics. • Delivers more accurate restoration forecasts to support proactive customer communications and outage notifications. • Optimizes field crew deployment and resource allocation to improve operational efficiency. • Provides near real-time insights to help utilities make informed decisions during outage events.

Microsoft technologies leveraged: • Azure Machine Learning for AI/ML model development, training, deployment, and continuous improvement. • Azure Data Services for data ingestion, processing, storage, and analytics. • Integration capabilities with Microsoft Customer Service, CCaaS, and utility operational systems to support end-to-end outage management processes.

Business outcomes: • Significantly improves restoration time prediction accuracy. • Reduces customer inquiry and call center volumes through proactive communication. • Enhances customer satisfaction and transparency during outage events. • Improves workforce productivity and field operations efficiency. • Provides a scalable and repeatable solution architecture for utility organizations adopting Azure-based AI solutions.