Labour forms a significant portion of total maintenance costs, yet it is also one of the most poorly managed areas of mine site operation.  Simply looking at previous requirements is a poor way to determine future need, and monitoring backlog is an ineffective indicator.

As industry experts in mining equipment and operations, Emeco understands the importance of managing maintenance and labour efficiently. We recently researched mine site data, developed by Red Button Group, to identify changing skill requirements over the life of site equipment.

Many mine sites struggle to manage labour efficiently, which has a significant impact on maintenance costs given the high labour component. To properly evaluate the issue, Emeco reviewed equipment maintenance data and labour requirements over time, and discovered four key areas of influence:

  • Total requirements for maintenance labour changes over the life of equipment
    In the beginning, labour intensity is low, building over time to the longer-term sustaining rates around the first major rebuild. This presents the opportunity for sites with new equipment to have a relatively low labour workforce in the initial years, and only ramp up as demand requires.
  • Labour requirements can be very ‘’lumpy’’
    Over the equipment lifespan, labour intensity changes significantly.  It is often assumed that the variance in labour requirement will even-out across large fleets, but this is not shown in the data.  Even in very large fleets the demand is lumpy over time.
  • Agility is essential for labour demand over time
    What was sufficient last year, may be insufficient this year.  There is an opportunity for most sites to better align the size of their maintenance labour team with demand to avoid backlogs that impact on machine reliability, or an oversupply of labour for the work required that leads to higher costs.
  • Labour skill requirements change significantly over time
    At the start of equipment life, semi-skilled resources are sufficient for most tasks required. As the equipment ages, the skill levels increase and specialist trade skills are needed. For example, after 15,000 hours a spike in fitter skills can be seen and after 20,000 hours the demand for HV electrical skills increases for electric drive trucks. As the demand for labour fluctuates over time, there is an even greater shift in the specific skills required to complete the work to a high quality and low Mean Time To Repair (MTTR).

Labour demand over time / insert relevant graph/s

This graph highlights that procurement skills are needed early on in a site’s equipment lifespan, with more maintenance-heavy skills required as equipment ages over time.

  • The experience required for maintenance resources changes over time
    In addition to considering trade skill levels, experience levels can also have an impact on labour efficiency and costs. Trade skills are the basis for fixing a known problem, but as equipment ages the greater challenge lies in being able to identify the problem to be fixed. In a nutshell, team experience levels can be the difference between a 10-hour downtime and one week of downtime.

There are many strategies that have the potential to improve a site’s labour cost base and create better asset performance outcomes. These include:

  • Benefiting from low levels of labour intensity early in an asset’s lifespan
  • Being agile in labour demand over the life of the asset and fleet
  • Managing skill levels for the work required over time
  • Actively managing the experience-base of the team to provide diagnostic skills.

The opportunity for cost recovery lies in being able to balance not only the total labour required, but the ongoing requirements of specialist trade skills. Mines can significantly reduce MTTR as a machine ages by ensuring that the right level of experience is part of every shift, and at times even creating specialist roles to perform this function on site.

By forecasting labour requirements based on skill levels is it possible to create an effective labour plan that uses internal and external resources in the best way to minimise costs. This approach also maximises the asset performance from a MTTR, Mean Time Between Failure (MTBF) and availability output.