Handling equipment is expensive, and utilisation figures reported as annual averages conceal both surplus and shortage.
What to measure
Hours in motion, from telematics, distinguished from hours powered on.
Hours available against hours demanded, by hour of day.
Time waiting for equipment, which appears as a gap between task assignment and first movement.
Charging and battery downtime, which in electric fleets is a real constraint.
Maintenance downtime, planned and unplanned.
Idle time in position, which distinguishes an unused machine from one parked between tasks.
The averaging trap
An annual average utilisation of thirty percent looks like a surplus.
The same fleet may be fully committed for four hours a day and idle the rest, in which case it is not surplus at all.
Always report by hour and by day of week, which is where the pattern is.
The peak determines the fleet size; the average determines nothing.
What the data typically shows
A fleet sized for the peak, idle for most of the shift.
Shortage concentrated at specific hours, usually after order release and before despatch cut-off.
A subset of machines doing most of the work, because of where they are parked or who prefers them.
Machines out of service longer than the maintenance record shows.
Charging gaps that could be shifted to non-peak hours.
Acting on it
Shift the demand before buying capacity. Moving a task type to an off-peak hour is free.
Stagger charging, which recovers capacity at the peak in electric fleets.
Rebalance parking positions, so machines are where the work starts.
Pool rather than assign equipment to individuals, which raises effective utilisation.
Then consider fleet size, which is a capital decision the data now supports properly.
Impacts and safety data
Telematics on handling equipment records more than movement.
Impact events, which are a safety signal and an early indicator of racking damage.
Speed in defined zones.
Operator access control, where the machine requires a competent operator to unlock it.
Pre-use check completion.
Treat impact data as a safety input first. Using it primarily for individual discipline produces under-reporting, which is the opposite of what the data is for, and this is covered in the safety section.
Maintenance from usage
Service intervals by hours in motion rather than by calendar, which is more accurate and usually cheaper.
Fault prediction from usage patterns, where the fleet is instrumented well enough.
Downtime cost quantified, which is the argument for spares and for preventive work.
What to report
Availability against demand, by hour, on one chart.
Waiting-for-equipment time, which is the operational consequence.
Impact events, trended, reported to whoever owns safety.
Utilisation by machine, which reveals the unused subset.
Downtime, split planned and unplanned.
Parking and staging positions
A free change that recovers real capacity and is rarely examined.
Plot where machines are at the start of each shift against where the first tasks are.
The gap is travel before any work happens, repeated every shift by every operator.
Move the parking positions to match the work start pattern.
Check charging access, which is usually why machines are parked where they are.
Re-check after any change in work pattern, because the optimum moves.
Charging as a capacity constraint
In electric fleets, charging is frequently the limit rather than machine count.
Plot charging demand against available charging points, by hour.
Look for machines waiting to charge, which appears as unavailability that the fleet size does not explain.
Shift charging out of the peak where battery chemistry allows.
Consider opportunity charging during breaks, which changes the arithmetic entirely.
Model the charging infrastructure before buying more machines, since additional machines without additional charging capacity add nothing at the peak.