Why the planned-to-unplanned ratio is the most important maintenance metric in mining
The single most revealing metric in mine maintenance is the ratio of planned to unplanned maintenance hours. An operation where 70–80% of maintenance hours are planned (PM, condition-based work, scheduled overhauls) is operating a fundamentally different maintenance system to one where 60% of hours are corrective breakdowns. The first can predict its maintenance schedule and its machine availability. The second responds to events as they happen - and the cost, the disruption to production, and the risk of cascading failures are all higher as a result.
Most CMMS systems contain the data to calculate this ratio. The problem is that CMMS data is rarely presented in a form that makes the trend visible, actionable and accessible to both the maintenance planner and the mine manager. Maintenance reporting bridges this gap.
The metrics that drive a maintenance and reliability dashboard
- PM compliance rate - % of preventive maintenance tasks completed on time within the scheduled window; the maintenance planner's primary KPI
- Work order backlog (hours) - outstanding corrective work by priority and asset; the leading indicator of future unplanned breakdowns
- Planned vs unplanned maintenance ratio - the single most important indicator of maintenance strategy effectiveness
- MTBF (Mean Time Between Failures) - average operating hours between failures of the same type on the same machine class
- MTTR (Mean Time To Repair) - average time to complete a repair once it starts; measures workshop efficiency
- Cost per operating hour - total maintenance cost divided by productive equipment hours; the financial integration of maintenance performance
- Parts fill rate and critical spare availability - % of parts requests fulfilled from stock on first request; the supply chain input to maintenance performance
Top failure mode analysis: fixing the same problem fewer times
The most actionable insight available from a CMMS is the identification of repeat failure modes - the same component, the same fault code, the same machine class, failing repeatedly. Repeat failures are a symptom of an unresolved root cause: wrong lubricant specification, operator loading behaviour, ground conditions exceeding design parameters, or a component quality issue. Reporting that ranks failure modes by cumulative downtime hours (not just event count) over a rolling 12-month window is what surfaces these patterns consistently, rather than leaving them to be discovered by the reliability engineer in an annual review.
From scheduled PM to condition-based maintenance: the data foundation
Condition-based maintenance (CBM) - triggering maintenance activities based on equipment health indicators rather than calendar intervals - is the direction the industry is heading. Oil analysis results, vibration data, thermal imaging, and component hour tracking are all inputs to CBM programmes. The maintenance dashboard is the platform that brings these condition indicators together alongside the CMMS work order history, so the maintenance planner can see which assets are showing early degradation signals before they fail, and the reliability engineer can calibrate interval thresholds based on actual failure history.
CMMS reports vs a reliability-focused maintenance dashboard
Native CMMS reporting vs unified maintenance dashboard
| Aspect | Native CMMS reporting | Unified maintenance dashboard |
|---|---|---|
| PM compliance visibility | Available in CMMS but rarely surfaced for management | Prominent - trended and by asset class, planner and area |
| Planned vs unplanned trend | Requires custom report or export + pivot | Continuous - updated daily with every work order close |
| Top failure mode analysis | Ad-hoc export; done quarterly at best | Rolling 12-month Pareto - always current |
| Link to fleet availability | CMMS and dispatch in separate systems | Maintenance hours linked to MA/PA impact in one view |
| Cost per operating hour | Requires ERP join - rarely done consistently | Calculated automatically from CMMS + equipment hours |
The Power BI and Fabric architecture behind maintenance reporting
On a typical SolveBI deployment we integrate CMMS work orders (SAP PM, IBM Maximo, Pronto), PM schedules, equipment hour records from dispatch, parts inventory transactions and ERP cost data into Microsoft Fabric, then expose a single maintenance model through Power BI. The maintenance planner sees PM compliance and the work order backlog view; the reliability engineer sees MTBF trends and failure mode Pareto analysis; the maintenance superintendent sees planned-vs-unplanned ratio and cost-per-hour - all from one dataset.
Common mistakes in mining maintenance reporting
- Tracking availability without PM compliance. Low availability is a symptom; poor PM compliance is often a cause.
- Counting failure events rather than failure hours. The failure that happens most often is not necessarily the one costing the most.
- No backlog management view. An unseen or unmanaged backlog of corrective work is a leading indicator of the next wave of breakdowns.
- Maintenance and operations data in separate systems with no integration. The link between maintenance hours and equipment availability only becomes visible when both datasets are in the same model.
- MTBF calculated globally rather than by failure mode. Global MTBF improvement can hide a specific repeat failure mode that is getting worse.
From a CMMS export to a reliability dashboard that surfaces the next breakdown before it happens.
Book a free 30-minute consultation with a SolveBI consultant. We'll map your CMMS, dispatch and equipment data, agree the right maintenance reporting structure, and quote a phased Power BI deployment you can budget against.
