Solutions · Predictive maintenance
Predict the failure. Protect the promise.
ExlAthena forecasts when an asset will fail and shows what that does to capacity, MRP and customer promise dates, so maintenance and planning act on the same decision instead of two separate systems.
What is predictive maintenance?
Predictive maintenance uses live sensor, historian and maintenance data to forecast when a machine is likely to fail, so work is scheduled just before failure rather than on a fixed calendar or after a breakdown. ExlAthena goes one step further: it shows what the forecast failure does to capacity, orders and promise dates.
What you get
- Remaining useful life (RUL) estimates with uncertainty bands
- Anomaly detection against each asset's golden profile
- Dynamic FMECA, criticality ranking and root-cause analysis
- Maintenance window optimisation and what-if scenarios
- CMMS integration following the ISO 14224 work-order lifecycle
- KPI translation from model metrics to downtime cost and OTIF impact
How it works
How ExlAthena predictive maintenance works
- 01
Sense
Vibration, temperature, pressure, current and run-hour signals stream in from IoT sensors, historians and SCADA, alongside CMMS work-order history.
- 02
Detect
Anomaly detection compares each asset against its own golden operating profile and flags drift long before an alarm limit is crossed.
- 03
Predict
Prognostic models estimate remaining useful life (RUL) and failure probability with an uncertainty band, so planners see a failure window, not a single guess.
- 04
Diagnose
Dynamic FMECA and root-cause analysis rank the likely failure mode and its criticality, so the right spare and the right crew are lined up.
- 05
Decide
An optimisation solver picks the maintenance window that costs the least output, and the capacity impact flows straight into MRP and promise dates.
- 06
Close the loop
Work orders sync back to the CMMS, and a post-maintenance check confirms the asset recovered so the models keep learning.
Supply chain impact
Why predictive maintenance belongs in supply chain planning
Capacity stays honest
A forecast failure window reduces available hours on that line before it happens, so the production plan is built on real capacity.
Promise dates hold
Orders that depend on the at-risk asset are re-sequenced or moved to an alternate line, and customers get dates the plant can keep.
Spares arrive on time
The likely failure mode drives a spare-part purchase order early enough to cover supplier lead time.
Answers
Predictive Maintenance questions, answered
What is predictive maintenance?
Predictive maintenance forecasts when equipment is likely to fail from live condition data such as vibration, temperature and current, so maintenance is done just before failure. It sits between reactive maintenance (fix after breakdown) and preventive maintenance (fix on a fixed schedule).
How is predictive maintenance different from preventive maintenance?
Preventive maintenance follows a calendar or run-hour schedule whether the asset needs it or not. Predictive maintenance uses the asset's actual condition to decide when work is needed, which avoids both unnecessary servicing and unexpected breakdowns.
What is remaining useful life (RUL)?
Remaining useful life is the estimated operating time an asset has left before it fails or falls below an acceptable performance level. ExlAthena reports RUL with an uncertainty band so planners can see a failure window rather than a single date.
What data do you need to start predictive maintenance?
Typically sensor or historian data such as vibration, temperature, pressure or motor current, plus maintenance history from the CMMS. ExlAthena can start with the assets that already have data and widen coverage over time.
Does ExlAthena replace our CMMS?
No. ExlAthena reads work-order history from the CMMS and writes recommended work orders back to it. The CMMS remains the system of record for maintenance execution.
How does predictive maintenance affect supply chain planning?
A forecast failure reduces available capacity on that line. ExlAthena passes that capacity impact into MRP, production sequencing and promise dates, so orders are moved or re-dated before the breakdown rather than after it.
Which AI models are used for predictive maintenance?
ExlAthena uses an ensemble of methods, including gradient-boosted trees, LSTM neural networks and time-series foundation models, chosen per asset class and validated by backtesting before they drive decisions.
Which industries use predictive maintenance?
Discrete and process manufacturing, automotive and industrial suppliers, chemicals, pharmaceuticals, food and sugar processing, and logistics fleets all use predictive maintenance on critical rotating and production assets.
Next step
See predictive maintenance on your own plant data
A discovery workshop on live ERP, MES and asset data shows where the first decision pays back.