Reliability analytics
End the day knowing where the plant stands.
MaintenanceIQ reliability analytics turns the work your team logs into OEE, MTBF, MTTR, PM compliance, bad-actor rankings, maintenance cost and spares advice. It helps your plant head decide on spares, people and replacements with facts instead of a spreadsheet built last month.
17:00 · Plant head · Illustrative
The day's work, already turned into numbers.
An illustrative evening. At 17:00 your plant head opens the Reliability page. The bearing change on pump P-07 is there as planned work, not a breakdown. PM compliance for the week is on track. Press HP-11 still tops the bad-actor list for downtime this month, and its maintenance cost by area makes the case for a closer look at the hydraulics.
None of it was assembled by hand. Every figure comes from work orders, breakdowns, PMs and OEE shift logs recorded today, so the numbers move as the plant moves. The plant head walks into tomorrow's meeting with the same facts the planner and technicians are working from.
P-07 and HP-11 are illustrative machines.
- Planned vs reactive work and PM compliance
- Bad actors by downtime and spend
- Trend charts over time, on the Reliability page and the dashboard
Reliability
See which machines are costing you the most.
A plant head has limited hours and budget. The bad-actor ranking puts the assets with the most downtime and spend at the top, so attention goes where it pays back. Next to it sit the measures that show whether maintenance is getting ahead: mean time between failures, mean time to repair, downtime, the split between planned and reactive work, and PM compliance.
All of them come from breakdowns and work orders your team already records. There is no separate data entry for reporting, and trend charts show whether a fix is holding.
- MTBF and MTTR per asset and across the plant
- Downtime and planned vs reactive work
- PM compliance
- Bad-actor ranking by downtime and spend
OEE
OEE without a separate MES.
Overall equipment effectiveness shows how much of a machine's planned time produced good output. MaintenanceIQ works it out per machine and across the plant.
OEE = Availability x Performance x Quality- Availability
- Run time as a share of planned time. MaintenanceIQ derives the downtime from the breakdowns you log.
- Performance
- Actual output against the ideal rate for the time the machine ran, from the shift log picked up from the machine's sensors, or entered by the supervisor where a machine has no sensor.
- Quality
- Good units as a share of units made, from the same shift log.
The shift log carries the planned minutes, the units made and the good units, against the ideal rate set for the asset. It arrives from the machine's sensors through the same token-secured ingest used for condition monitoring. A machine that ran zero minutes reads 0, and missing inputs show as blank rather than a guessed number.
One shift on press HP-11
Worked example (illustrative)
Press HP-11 was planned to run 480 minutes. A logged breakdown took 48 minutes. The ideal rate is 2 parts a minute, it made 760 parts, and 722 were good.
| Factor | Arithmetic | Result |
|---|---|---|
| Availability | (480 minus 48) / 480 = 432 / 480 | 0.90 |
| Performance | 760 / (432 x 2) = 760 / 864 | 0.88 |
| Quality | 722 / 760 | 0.95 |
| OEE | 0.90 x 0.88 x 0.95 | 0.75 |
OEE is about 75 percent, and the A, P and Q breakdown shows that performance lost the most. The figures are examples, not a benchmark. Try your own numbers in the OEE calculator.
Cost and spares
Put a cost on every breakdown, and stock the right spares.
Decisions on spares and replacements need money in them. Maintenance cost analytics adds up the parts used and the charges recorded against each breakdown, so your team can confirm what each breakdown cost, then rolls it up by asset and by area, in the one currency your organization works in.
Spares optimization looks at how fast each part is used and how much is on the shelf. It shows days of cover, flags stockout risk, overstock and dead stock, and suggests reorder points, so money is not tied up in parts nobody uses while the critical bearing runs out.
- Parts used and breakdown charges by asset and area
- Days of cover for each part
- Stockout risk, overstock and dead stock flags
- Suggested reorder points
Reports
Build the report once, then let it arrive.
Managers and admins build a filtered report from six datasets: work orders, breakdowns, PM compliance, calibration, assets and parts. Filters fit the dataset, such as date range, factory, department, status, type, priority or criticality. Preview the result, export it to CSV, and save the setup under a name the whole organization can reload.
A saved report can be scheduled daily, weekly or monthly. When it is due it rides along in the morning operations digest, in its own Scheduled reports section with an in-app notification, so nobody gets one more email. Separately, every Monday a weekly reliability report goes to your admins and managers.
- Six datasets with filters that fit each one
- CSV export and saved, shared report setups
- Scheduled reports in the morning digest
- Weekly reliability email on Mondays
Go deeper into reliability analytics
- OEE softwareHow OEE is captured and where the losses are.
- MTBF and MTTR reliability analyticsFailure and repair measures in detail.
- Maintenance cost analyticsWhat each breakdown cost, by asset and area.
- Spares optimizationDays of cover, stockout risk and reorder points.
- MTBF and MTTR calculatorWork out both from your own figures.
Frequently asked questions
Where does the data for these numbers come from?
From the work your team already records in MaintenanceIQ: work orders, breakdowns, PMs, parts transactions and the OEE shift logs your machine sensors send. There is no separate reporting database to keep up to date.
Does OEE need sensors on every machine?
No. Availability is derived from the breakdowns you log. Performance and Quality come from shift logs picked up from your machine sensors, and where a machine has no sensor, the supervisor enters the shift log by hand.
Can we export the data?
Yes. The report builder exports any of its six datasets to CSV, and saved reports can be scheduled to arrive in the morning operations digest.
Who receives the weekly reliability report?
Admins and managers receive it by email every Monday.