Article 1: Measuring Equipment Performance
Mining companies must use every possible strategy to cope with the pressure to produce more while coping with lower-grade deposits and meeting rising sustainability targets.
One strategy often overlooked is measuring equipment performance, all the way from pit to plant.
Typical equipment performance measurement
The way mining companies measure the performance of their equipment fleets today varies from company to company, but most use a Time Allocation Model or TAM.
Under this model, mining engineers gather data related to equipment hours using paper log sheets or a Fleet Management System. They then allocate that data to measurement groups – usually Operating, Delay, Standby, Downtime, Planned Maintenance and Unplanned Maintenance – that, added together, equal total Calendar Time. The engineers then use these measures in various formulas to create KPIs, such as availability and utilization.
TAM remains a manual process at many mines, where engineers type numbers into a spreadsheet and create a chart like the one below:
However, charts like these do not promote detailed analysis or make it easy to compare current results with historical performance.
Improved equipment performance measurement
Advanced mine operations management – such as that offered through Dassault Systèmes’ Mine Operations Management (MOM) solution – can expand the typical Time Allocation Model to include both fleet management and equipment operator data. This in turn allows mines to record and analyze all haulage equipment and operator activities, and not only connect each movement with the associated loading and haulage equipment but also with the operator of the equipment.
From there, mines can visualize the patterns in equipment performance and detect important issues, such as:
- carry-back – material stuck in the tray after unloading, reducing the tray capacity
- under-loading to reduce wheel spinning, especially on steep ramps in wet weather
- maintenance issues with a particular truck fleet
- higher than expected unplanned downtime in the maintenance department
- over-reported truck counts, and
- under-loaded excavators or incorrect excavator loading.
They can also identify:
- crew, supervisor, shifts, excavators and truck operator performance
- lower-than-expected production or differences in production between shifts
- lower-than-expected excavator production
- incorrect driver positioning of trucks for loading
- incorrect recording of truck counts, and
- if additional training, equipment upgrades or other steps need to be taken in order to improve production.
Measuring costs
This equipment/operator performance information can also be integrated with other data gathered from pit to plant to create a 360-degree view of the mining process.
Take, for example, the process of moving material from the pit to a stockpile.
A mine can take geological information about material qualities, quantities and locations in pit areas and enrich it with equipment performance data – a 5 km effective flat haul cycle results in a fuel burn rate of x l/km, number of loads to the stockpile per shift, excavator loading time versus time waiting for a truck, etc. – to measure the actual costs associated with each activity (aka activity-based costing).
The mine can then use that information to examine cost against material quality, equipment utilisation and material flow, and the effectiveness of mine plans/design, and the efficiency of fleets, shifts, crews and employees – and chart a new route to improved pit-to-stockpile performance.
Next week, in Article 2 of this series, we discuss how to use advanced mine operations management to track grade variances and grade control.
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