Why maintenance reporting is operations reporting
Maintenance is often treated as a cost centre - measured by spend, judged by spend, optimised for spend. The plants that out-perform their peers treat maintenance as an operational lever instead. Good maintenance reporting is what makes that shift possible. It connects asset condition, downtime, planned vs. reactive work and cost into a single picture that operations and maintenance leadership share.
The fundamental question maintenance reporting answers is simple: are we getting more reliable, or less, and what is driving the change? Almost every other maintenance decision becomes easier once that question has a credible answer.
Reactive, preventive, predictive - knowing which mix you have
Most manufacturing organisations run a mix of three maintenance strategies, and the mix itself is one of the most important reporting outputs:
- Reactive maintenance - fixing equipment after it breaks. Highest cost, highest disruption, but unavoidable for some assets.
- Preventive maintenance - scheduled work based on time or usage. Reduces breakdowns but can over-service some assets.
- Predictive maintenance - condition-based work, triggered by sensor or process signals. Highest value when the data and analytics are in place.
A useful reporting layer shows the actual mix - hours, dollars and incidents in each category - and trends it over time. A plant that says it is predictive but whose dashboard shows 60% reactive work has its first improvement project clearly identified.
The reliability metrics worth reporting
The maintenance team's daily metrics differ from the executive's monthly metrics, but they should sit on the same underlying data. The shortlist most of our clients converge on:
- Mean time between failures (MTBF) - how reliable each critical asset is becoming over time
- Mean time to repair (MTTR) - how fast the team recovers when a failure does occur
- Planned maintenance percentage - the share of total maintenance hours that were planned rather than reactive
- Downtime cost - lost production attributable to maintenance events, in dollars
- Backlog hours - outstanding planned work, by priority and asset
- Work-order completion rate - the share of planned work completed on schedule
Where maintenance reporting data lives
Modern maintenance reporting depends on bringing several systems together. The CMMS holds work orders, schedules and labour. The ERP holds spares cost and inventory. Shop-floor and IoT systems generate the condition signals - vibration, temperature, current, run-time - that make predictive maintenance possible. Each system is correct on its own; the value comes from joining them.
On a typical Microsoft Fabric and Power BI deployment, we ingest each of these on a defined cadence, model them in a Lakehouse, and surface a single reliability view that the maintenance and operations teams share. The CMMS remains the system of record - the dashboards never write back to it.
Predictive maintenance - what is realistic, what is hype
Predictive maintenance has been over-marketed for a decade, but the underlying idea is sound: certain failure modes give early warning signals in sensor data, and a system that watches for those signals can trigger maintenance before the failure happens. The honest version of the story is that this works extremely well for some assets and failure modes, and not at all for others.
Where predictive maintenance pays off - and where it does not
| Asset / failure mode | Hype claim | Realistic outcome |
|---|---|---|
| Rotating equipment (motors, pumps, compressors) | Predicts everything | Vibration and current-trend monitoring genuinely catches most bearing and imbalance failures early |
| High-cycle moving parts | AI will replace inspection | Condition signals supplement, not replace, scheduled inspection |
| Sudden brittle failures | Predictive saves the day | Often very little warning - design or redundancy is the right answer |
| Wear-driven failures | Trivial | Excellent fit - run-hours and load data are usually enough to schedule before failure |
How Power BI ties reliability data into one operating view
On a typical SolveBI deployment the CMMS, IoT sensors, ERP and shop-floor data land in Microsoft Fabric, with a single reliability model exposed through Power BI. Technicians see a focused work-order dashboard, supervisors see crew utilisation and backlog, plant managers see availability and unplanned downtime, and executives see cost-of-failure trends - all from the same Power BI dataset. The CMMS stays the system of record; the Power BI model is what every audience actually opens.
What executive maintenance reporting should look like
The executive view of maintenance is rarely the same as the technician's view, but they should share the same data. The standard pattern we use:
- Headline reliability - planned maintenance percentage, total downtime cost, reactive work hours - all with target lines
- Top assets by downtime - the Pareto that points the next reliability project
- Backlog trend - whether planned work is being completed faster than new work is arriving
- Maintenance cost vs. budget - by asset class, with the share that was reactive called out separately
- Drill-through - to individual work orders, asset history and the underlying sensor data
Common mistakes in maintenance reporting
- Reporting only spend. Spend tells you what you used; it does not tell you whether the equipment got more reliable. Reliability metrics belong alongside cost from day one.
- Treating reactive work as a fact of life. The reactive percentage is movable, and the dashboard should be designed to make moving it the obvious next project.
- No link to lost production. Maintenance downtime expressed in hours is informative; expressed in lost-margin dollars is actionable.
- CMMS data alone. Without joining to operations data, the dashboard can show planned work completion but not whether reliability is actually improving.
- Over-investing in predictive too early. Predictive maintenance pays off after the basics - work-order discipline, planned vs. reactive ratio, asset criticality - are in place.
From firefighting to a managed reliability programme.
Book a free 30-minute consultation with a Microsoft-certified SolveBI consultant. We'll map your current maintenance data sources, agree the right reliability metrics, and quote a phased Power BI deployment you can budget against.
