Solutions · Predictive maintenance

Catch failure before failure catches you.

The Intelligence layer continuously scores asset health from meter, work order, and inspection signals. When the score declines, the platform opens a recommended work order with the signals that drove the decision attached.

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The problem

The reactive maintenance trap

Most operations still run on calendar based preventive maintenance and reactive break fix. Critical assets are over serviced when they are healthy and unattended when they are degrading. The cost shows up in two places: planned maintenance budget burnt on healthy equipment, and unplanned downtime that derails operations.

Calendar PMs over service healthy equipment
Reactive break fix is expensive and disrupts production
Failure patterns are invisible across distributed estates
Operators carry the institutional memory, not the system

What the platform brings

The capabilities behind the use case.

Concrete behaviours of the platform, not feature checkboxes. Each capability is exposed in the product and traceable to an operational outcome.

Continuous health scoring

Per asset health score derived from meter signals, work order history, inspection findings, and environmental thresholds. Updated every shift, exposed on the asset detail screen.

Signal explainability

Every score change links to the signals that drove it. Operators see why a score declined, not just that it declined, which is the difference between trusted insight and a black box.

Automated work order generation

When a score crosses the inspection threshold, the platform drafts the work order with the recommended craft, parts, and lead time, ready for an approver to release.

Replaces calendar PMs over time

As condition data stabilises, calendar driven PMs are migrated to condition based triggers, freeing planned hours on healthy assets and concentrating them where the signals say they matter.

Outcomes

The change you can put in front of a steering committee.

Operational platforms are bought to move metrics. These are the changes Next EAM is designed to drive on this use case once operating data starts flowing through it.

Lower unplanned downtime

Degradation surfaces days or weeks before failure, with enough lead time to plan parts and labour.

Higher planned vs reactive ratio

Best in class operations sit at 80% planned. Predictive scoring is the lever that moves the needle.

Right sized maintenance budget

Healthy assets are not serviced unnecessarily; degrading assets are not missed. Capital and labour follow the signal.

Trust and compliance

Auditable, explainable, tenant scoped

Every score, every recommendation, and every auto created work order is linked to the user, signals, and policies that produced it. Models run inside your tenant boundary and never see other customers data.