Predictive asset analytics for an energy distributor

Failure-risk scoring across the network's assets, feeding prioritised field work — maintenance spend moved from reactive to planned.

Advanced AnalyticsIoTAzure

31%Fewer unplanned outages

40K+Assets scored monthly

PlannedMaintenance replacing reactive

The Challenge

An ageing network was maintained reactively: crews responded to failures rather than preventing them, and asset condition data sat in telemetry, inspection reports and work-order history that never met in one place.

Reactive maintenance across an ageing network

Condition data siloed in three systems

No forward view of failure risk

The Solution

We engineered pipelines over telemetry, inspections and work-order history into one asset model on Azure, and put failure-risk scoring on top — monthly scores for every asset, turned into a prioritised field work programme.

One asset model over telemetry, inspections and work orders

Monthly failure-risk scores across the network

Risk ranking driving the field work programme

THE IMPACT

"The crews now go where the risk is, not where the last fault was."

From reactive to planned Energy Distribution Utility