Most network maintenance is either scheduled by the calendar or triggered by a failure. Both are expensive: one services healthy equipment, the other arrives after customers are already affected. Predictive maintenance uses the history your network keeps about itself to point field teams at the equipment that needs attention next.
From failure history to a daily work list
The method is the same whether the target is access nodes, backhaul links, gateways or customer premises devices.
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Define failure
Agree with operations what counts as a failure for each equipment type, and how far ahead a warning is useful.
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Build the analytic record
Combine performance, alarm, configuration, maintenance and environmental history into one record per element.
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Train and test models
Fit prediction models to past failures and measure how many they would have caught, and how many false alerts they would have raised.
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Deliver a ranked list
Give maintenance teams a regularly refreshed list of the highest-risk elements, with the factors driving each score.
Outputs your teams can act on
The value comes from changing what field teams do each day, so the outputs are built around their workflow.
At-risk equipment list
A prioritized list of nodes or devices, sized to the capacity of your maintenance teams.
Failure drivers
The conditions most associated with failure for each equipment type, useful for engineering and vendor discussions.
Model performance tracking
Ongoing measurement of how well predictions match what happens, so the models are retrained when they drift.
Integration with dispatch
Feeds into ticketing and workforce systems so predicted work is scheduled alongside reactive work.
Data we typically work with
Start small, prove the value, then scale
We would rather show a result on your own data than promise one on a slide. Each engagement moves through three stages, and you decide at the end of each whether to continue.
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Set up a sandbox
We agree the questions that matter, stand up a secure analytics environment and load a first extract of your data.
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Prove one use case
We build and test the models for a single, well-defined use case and review the findings with the teams who will use them.
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Extend and operationalize
Use cases that prove their value are hardened, automated and put in front of planners and operations teams as part of their daily tools.
See which equipment is at risk
Share the history for one equipment type and we will show what a prediction model would have caught.
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