Field service and workforce
Predictive Maintenance
Condition intelligence that becomes scheduled work orders under the approval policy you set, measured in downtime and maintenance cost.
The problem
Calendar-based maintenance services healthy assets and misses failing ones. An alert that never becomes a scheduled work order with parts allocated is only a dashboard, and frequent false alarms teach planners to ignore the rest.
Who owns it
The reliability engineering lead. Maintenance planners schedule the work, technicians close the loop, and plant management reviews downtime economics.
What it learns from
- Sensor and telemetry streams from the historian
- Maintenance work-order history, including free text
- Parts and ERP
- Operating context, such as load and product mix
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Detects degradation and predicts failure
Condition baselines for each asset, adjusted for operating regime, flag abnormal behavior. Where failure history exists, models predict failure probability and remaining useful life. Failure events are mined from work-order text, and sensor health is monitored so a sensor fault is not mistaken for wear.
- 02
Decides through an alert budget and an approval policy
Severity maps to priority, alert volume is matched to maintenance capacity, and thresholds are tiered by asset criticality. For each asset class you choose whether work orders are drafted, approved, or created automatically.
- 03
Acts in the CMMS
Draft work orders with a diagnosis, priority, recommended parts, and a due window land in the maintenance-management system, bundled with planned windows. Healthy assets get deferral recommendations, and closeout captures whether each finding was real.
- 04
Measures against a recorded baseline
Downtime baselines are recorded before work starts. A phased comparison by asset class tracks downtime hours, maintenance cost, and the share of failures caught, and technician closeouts confirm each alert.
What it moves
The business levers
Unplanned downtime
Fewer failures in service, caught early enough to schedule parts and labor.
Maintenance cost
Fewer calendar tasks on healthy assets and fewer expedited parts.
Secondary damage
Failures stopped before they damage neighbouring equipment.
Safety exposure
Fewer unplanned interventions on failing equipment.
Proof
How the return is proven
The return is the change in downtime and maintenance cost on the covered asset classes against the recorded baseline, stated against the return model agreed up front. Technician-confirmed alert precision, the measure planners trust, is reported beside it.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Historian access is read-only, and asset views are scoped by site and role through your single sign-on.
Approval gates
Work-order autonomy is set per asset class, and changes to alert suppression go through management of change.
Audit trail
Every alert, draft, approval, execution, and closeout is logged in the maintenance system.
Safety boundary
The solution writes work orders and nothing else. It never writes to control systems or safety-instrumented systems.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.