Solutions · Predictive maintenance
Catch failure before failure catches you.
The Intelligence layer scores asset health with explainable rules from PM compliance, failure rate, meter velocity and asset age. When the score declines, it shows the signals that moved it, and a planner can raise a work order with that evidence attached.
Asset health score
62 / 100
DecliningSignals analysed
Representative of the in-product Intelligence view, the signals that drove the decision are attached to the work order.
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
A rule-based 0–100 health score per asset from four signals: PM compliance, failure rate, meter velocity and age against expected life. It shows “unknown” when fewer than two signals are measured, and appears 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 — the difference between trusted insight and a black box.
Automated work order generation
When a score crosses the inspection threshold, the platform drafts the WO with the recommended craft, parts, and lead time, ready for an approver to release.
PM interval recommendation
For each PM, the AI compares the configured interval to actual MTBF and recent failures. Suggests tighter intervals on at-risk assets and looser intervals where you are over-maintaining.
KB-grounded explanations
When the assistant recommends a corrective action, it cites the standard operating procedure or troubleshooting article from your Knowledge Base — not a generic best-practice guess.
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 NextEAM is designed to drive on this use case once operating data starts flowing through it.
Lower unplanned downtime
A falling score shows which signals moved, so planners can line up parts and labour before the work becomes urgent.
Higher planned vs reactive ratio
Planned work costs less than reactive work: the US DOE / PNNL O&M Best Practices guide puts preventive maintenance savings at 12–18% over reactive. Asset health scoring shows where to plan first.
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. AI answers use your tenant's data only, and personal data is masked before a cloud AI call.
Other ways teams use the platform
Work order execution
End to end work order lifecycle on a single audit trail — from request through closure.
Read morePreventive maintenance
Calendar and meter driven schedules with automatic work order generation, forecasting, and route grouping.
Read moreCompliance and audit
KSA aligned audit trails and one click compliance packs that replace weeks of pre audit data assembly.
Read more