Why fleet availability is a bigger cost lever than buying new trucks
On a large open-cut mine the haul fleet is the single biggest variable cost. A fleet of 30 large haul trucks burning diesel around the clock represents tens of millions of dollars in operating cost each year - and most of the variation in that cost traces back to how many of those trucks are available and how effectively they are being used when they are. A 1% improvement in mechanical availability on a typical large truck fleet recovers the equivalent of one additional truck's production output without a single capital dollar spent.
Yet in many operations the daily fleet health picture is assembled from CMMS work orders, dispatch delay records and workshop whiteboards - across systems that speak different languages, on a lag that makes the previous shift's breakdown the first thing the maintenance planner learns about at the morning meeting. Best-practice fleet reporting closes this loop by delivering a single, live equipment-health view that tells the shift supervisor what is available right now, and tells the maintenance planner what will be available tomorrow.
The four availability metrics and what each one tells you
Mechanical Availability (MA) measures the proportion of scheduled operating time that equipment is not under mechanical repair. It is the primary measure of maintenance effectiveness - and the one number the maintenance manager is accountable for. Physical Availability (PA) is similar but excludes operator delay time, giving a purer view of mechanical performance. Utilisation Availability (UA) measures how much of the available time the machine is actually being used - the operations team's number. Effective Utilisation (EU) combines both: it is the fraction of calendar time the machine is doing productive work. High MA with low EU often means operations is not using the equipment the maintenance team is providing - a management conversation, not a maintenance one.
Delay time analysis: where the hours go
Fleet time accounting classifies every machine-hour into one of four states: operating, standby, delay, and maintenance. The granularity within each category is where the value lies. Maintenance hours split into planned (PM), unplanned corrective, and waiting for parts - each demanding a different management response. Delay hours split into operational delays (wait for fuel, wait for blast, traffic), standby for lack of work (fleet oversupply) and operator delays (fatigue breaks, pre-starts). A dashboard that only reports total downtime without this classification is measuring the symptom, not the cause.
Ranking failure modes: fixing the same thing fewer times
In most fleets, 30–40% of all unplanned breakdown hours trace back to the same top-10 failure codes. A suspension failure on a specific truck that accounts for six repair events in a quarter is not bad luck - it is a pattern. Reporting that ranks failure modes by total downtime hours (not just count of events) is what makes this pattern visible and actionable. The truck that fails once for 48 hours costs more than the truck that fails 12 times for 2 hours each - but only the hours-based view reveals that.
CMMS reporting vs live fleet dashboard: what changes
CMMS-only reporting vs unified fleet availability dashboard
| Aspect | CMMS-only reporting | Unified fleet dashboard |
|---|---|---|
| Visibility of current fleet status | Last closed work order - hours old | Live delay status from dispatch, combined with CMMS |
| MA vs EU relationship | Calculated separately, rarely compared | Side-by-side - highlights scheduling vs maintenance gaps |
| Top failure mode analysis | Export + pivot table, done ad-hoc | Continuous - ranked by downtime hours, updated daily |
| Planned vs unplanned split | Available but rarely surfaced clearly | Prominent - drives PM compliance accountability |
| Bench-to-board view | Multiple reports across systems | Shift supervisor, planner and manager see same data |
The Power BI and Fabric architecture behind fleet reporting
On a typical SolveBI deployment we land CMMS work orders (SAP PM, IBM Maximo, Pronto), dispatch delay records (Modular DISPATCH, Minestar), fuel transactions and preventive maintenance schedules into Microsoft Fabric, then expose a single fleet-performance model through Power BI. Shift supervisors see the live fleet-health view; maintenance planners see PM compliance and the PM-vs-CM ratio; fleet managers and mine managers see availability trends and cost-per-hour - all from one dataset with consistent definitions of MA, PA and EU applied across every machine type.
Common mistakes in equipment availability reporting
- Reporting MA only. Without EU alongside it, high availability that is not being used looks like success.
- Averaging across fleet types. A mixed fleet of trucks, excavators and drills has very different availability benchmarks - averaging them obscures every individual issue.
- Ranking failures by count, not hours. The failure that happens most often is not necessarily the one costing the most downtime.
- No planned vs unplanned split. Total maintenance hours do not reveal whether the maintenance strategy is working - the planned/unplanned ratio does.
- CMMS data only. Without dispatch delay records, a significant share of non-operating time is invisible to the maintenance picture.
From a workshop whiteboard to a live fleet health view before the morning meeting.
Book a free 30-minute consultation with a SolveBI consultant. We'll map your CMMS, dispatch and maintenance data, agree the right availability structure, and quote a phased Power BI deployment you can budget against.
