Thinklytics

Regional Electric Utility · Energy & Utilities · Denver, CO · 14 weeks

Grid reliability analytics deployed to 180 operations staff

A regional electric utility with 1.4 million customers struggled because its grid reliability data was scattered across six systems, including SCADA, OMS, GIS, and work management. Operations teams lacked a unified view, leading to slower restoration decisions. We developed a grid reliability analytics platform that cut the average outage restoration time by 34 minutes.

Challenge

The utility’s operations center relied on six separate screens, each showing a different system. Coordinators had to manually piece together outage status, crew locations, equipment history, and grid topology. This disjointed setup added 30 to 45 minutes to every restoration decision during outages.

Approach

We combined SCADA, OMS, GIS, and work management data into one platform to give operators a clear view of grid reliability. We created a real-time outage dashboard that automatically suggests crew dispatches based on location, equipment, and past restoration data. We developed a predictive model that scores equipment failure risk using five years of maintenance and failure records.

Outcome

We cut the average outage repair time by 34 minutes. Streamlining crew dispatch cut overtime expenses by $1.4 million annually. Using predictive failure scores, we flagged 142 high-risk assets and scheduled maintenance ahead of issues, stopping about 18 major outages in the first year.

We grabbed data from six different systems to really understand what’s going on.

The ops center was running six different systems at once, which made decision-making slow and messy, plenty of mistakes slipped through. So, we built a data integration layer that pulled real-time info from SCADA, OMS, GIS, and work management systems into a single dashboard. Just like that, restoration coordinators could instantly see outages, crew locations, equipment history, and the grid layout. It totally cleaned up their workflow.

How we spotted gear problems before they stopped us cold

We rolled up our sleeves and sifted through five years of maintenance logs, failure reports, gear ages, and environmental data. Then, we built a model to spot which assets were likely to fail. Every month, it scanned all 14,000 distribution pieces and flagged the top 142 at risk. This let the utility team focus on the troublemakers, slashing downtime and repair costs.

Results

  • 34 minutes Reduction in mean time to restore per outage
  • $1.4M Annual overtime cost reduction
  • 142 High-risk assets identified for proactive maintenance
  • 18 Estimated major outage events prevented in year one

Before, our restoration coordinators had to juggle six different screens during outages. Thinklytics built one dashboard that cut our average repair time by 34 minutes. Saving that much time on every outage really adds up and makes a big difference for our customers.

VP of Grid Operations, Regional Electric Utility

In the client's terms

The question

What decisions is this reporting supposed to support?

The situation

The operations centre ran six separate screens. During an outage a coordinator assembled outage status, crew locations, equipment history and grid topology by hand, adding 30 to 45 minutes to every restoration decision.

What we found

The delay was in assembling the picture, not in making the call. Coordinators were doing integration work under time pressure, at the exact moment when their attention was worth most elsewhere.

What it produced

Mean time to restore fell by 34 minutes per outage. Better dispatch cut overtime by $1.4M a year. The failure model identified 142 high-risk assets for proactive maintenance, preventing an estimated 18 major outages in year one.

The roadmap

SCADA, OMS, GIS and work management data were combined into one operational view. A real-time outage dashboard recommends crew dispatch from location, equipment type and restoration history rather than leaving the coordinator to work it out. A predictive model scores equipment failure risk from five years of maintenance and failure records.

The work behind this result

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Frequently asked questions

How do you reduce outage restoration time?

Remove the assembly work. Coordinators at this regional electric utility were building the picture from six separate screens under time pressure, adding 30 to 45 minutes to every restoration decision. One integrated view cut mean time to restore by 34 minutes.

Can equipment failures be predicted on a distribution grid?

With enough history. Five years of maintenance logs, failure reports, asset ages and environmental data trained a model here that scores all 14,000 distribution assets monthly and surfaces the top 142 by risk, preventing an estimated 18 major outages in year one.

What does better crew dispatch save?

$1.4M a year in overtime at this utility, because dispatch recommendations now come from location, equipment type and restoration history rather than being worked out by a coordinator mid-outage.