Minimize service disruptions and reduce maintenance costs by identifying network equipment and infrastructure at highest risk of failure before outages occur.
Equipment-level propensity scores for failure or degradation, including risk deciles and centiles for prioritization
Identify key indicators of equipment failure (e.g., usage patterns, environmental factors, equipment age, performance metrics)
Performance assessment including predicted downtime reduction, maintenance cost savings, and ROI simulation
Optimize maintenance scheduling and resource allocation with data-driven prioritization
This describes how far ahead in the future this model can be trained to predict. Ideally, the historical data available for training would be 2-3 times longer than the prediction window.
Accurately predict and prioritize high-risk equipment.
Maintain network reliability with continuous model refinement.
A typical modeling engagement with GlorifAI lasts 3-4 weeks once we get access to the data and proceeds in three distinct phases.
