Five facts about maintenance labour

Overview
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.

Challenge
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.

Results
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.

Summary
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.

Does mine size matter when it comes to Fleet Management Systems?

Overview
An integral part of managing production at a mine site is having accurate, readily-accessible data. This empowers key stakeholders in the management team to drive production reporting, materials tracking and budget forecasting. If a site does not run a Fleet Management System (FMS), manual input is heavily relied upon increasing the risk of human input error. Errors can drive outcomes that work against increased production at a reduced cost.

As each department of a mine relies on accurate information, the consequences for errors becomes greater. Errors in production reporting may result in the mine trying to fix the wrong problem. For example, if the system reports higher machine hours than what they really are, it may appear that the production rate is lower than expected, in turn prompting supervisors to focus on machine productivity. The risk to the mine planning team is that longer term data does not reflect actual practice at the mine, resulting in incorrect cash flows projections that could lead to project failure.

Alternatively, a FMS can have improved accuracy but involves a high monetary investment. Such an investment is not always feasible for smaller mining operation exposed to current industry cost pressures. By working in partnership with our clients, Emeco came up with a cost-effective FMS solution for a mine of any size.

Challenge
In late 2015, Mungari White Foil open pit mine evaluated multiple fleet management systems with the aim to increase production efficiency in the open pit load and haul functions. Analysis of these options determined that due to dynamic pit conditions and medium-term mine life, a traditional FMS option would not provide the optimum outcome due to:

  • High capital cost
  • High operational costs
  • Data usability.

To find a way forward, management met with the mining experts at Emeco to discuss a bespoke solution.

Results
The Emeco Operating System (EOS) is a fleet management and mining technology platform that has been developed in alliance with sites such as the Mungari White Foil Operations. Using the leading-edge technology available through the EOS, the team at Emeco provided a valuable FMS for mines of any size. For this project, we ensured the system satisfied the criteria of a low-intensity hardware installation, materials tracking data validation processes, remote monitoring, identification of metrics to improve overall operating efficiency and providing the right information to key stakeholders.

The initial results from White Foil operations have shown the benefits of the EOS approach including the improved accuracy of information to predict an 18 per cent reduction in unit cost or a 21 per cent increase in production within a few months.

Summary
Traditional one-size-fits-all approaches to fleet management systems are a thing of the past, with industry pioneers like Emeco leading the way in tailored mining data and management solutions. There is a justifiable business case, for any mine to utilise accurate and timely information. Such information in relation to material tracking and production assets can be used to achieve leading practice unit cost and resource recovery of a mine.

The approach developed for the White Foil operations was designed to address the needs of a smaller operator that does not have the scale for a complex and costly fleet management system, but which still provides the level of detail and real-time feedback required to demonstrate production improvement and data accuracy results.

FMS data collection and management system criteria

  • Low up front capital cost
  • Greater accuracy than manual input system
  • Low operational expenditure and ongoing resourcing requirements
  • Low training investment for users
  • Captures key performance indicators (KPIs) and materials tracking metrics
  • Access to the information via any computer or tablet mobile device over a 3G connection
  • Ability to upgrade the system later, to take advantage of features of a high precision global positioning systems (HPGPS), and Collision Avoidance.

For further detail please refer to the following paper presented at the Open Pit Operators’ Conference 2016: “Getting Accurate Mine Information to the Right Teams to Drive Production Improvement” by Robert Beckman and Taylor Elms.

Direct maintenance costs: benchmarking for success

Overview
Choosing the right equipment for the job is a big driver of total $/t costs for a mine site. But potentially even more important is the way in which that equipment is maintained.

Direct maintenance costs vary widely between operators and equipment types. These costs include labour and components as well as preventable, corrective and accidental damage. They do not include tyres or tyre-changing costs, fuel, downtime costs, infrastructure costs such as cranage, or supervision and support labour costs.

Factors such as component life, work management systems, quality of work, component cost agreements, rebuild specifications and labour management all impact on costs over the life of the machine.  Therefore, it is generally the maintenance practices that drive outcomes.

So how do you ensure your site’s maintenance practices aren’t driving up costs? A good place to start is an expert review of your mining practices against benchmarked industry data. Emeco is highly experienced at identifying cost reduction opportunities in maintenance and mining processes.

Challenge
Maintaining a truck cost-efficiently will have a greater impact over time than the actual selection of the right truck to begin with. In other words, the decision to use a 150t versus a 190t is less important than the maintenance and operation of that equipment. However, getting both correct is the overall goal!

The following graph shows the range in direct maintenance costs for a CAT 793D truck, based on Red Button Group’s International Benchmarking Database. The database, sourced from more than 400 operating sites, has been normalised for operating conditions and lifecycle positions to provide a credible reference for site maintenance cost comparisons.

WHOLE OF LIFE COST – Industry Comparison

When reviewing the difference between efficient and poor maintenance performance, the following three drivers are the most important:

  • Major component hourly (cost driven by both component cost and life)
  • Scheduled servicing costs
  • Labour efficiency and effectiveness.

Results
Benchmark comparisons are valuable in identifying opportunities, however it is perilous to compare direct maintenance costs between sites without normalising for key factors as this can lead to very destructive actions. Enter Emeco’s production benchmarking process. Through the Emeco Operating System (EOS) we can combine an operational assessment and value driver tree to identify which actions will actually reduce mining costs.

Normalisation is the key
When looking at direct maintenance costs, it is not possible to validly compare a truck that is 10,000 – 15,000 hours in the lifecycle, with a truck that is 15,000 – 20,000 hours in the lifecycle.  The major components due will make a high-level comparison meaningless.

Likewise, comparing a remote site in Western Australia with a site in the well-supported Hunter Valley will not provide a valid comparison of component rebuild options and costs or labour rates.

By comparing your site’s performance against hundreds of similar operations, your mining team can set realistic, achievable targets which will increase overall productivity.

Summary
If you can measure it, you can improve it. The world’s biggest mining companies know this.

The way in which equipment is maintained is critical to the $/t performance of a site.  It’s important to know where you sit on the spectrum of maintenance performance, and what the levers are to drive efficiency over time.

Having access to credible benchmarking information and an expert team who is experienced in efficient maintenance practices will help in achieving this. At Emeco, we work with industry benchmarking leaders and platforms to support our clients in evaluating and improving $/t costs for mine sites. By benchmarking first, we offer valuable, targeted recommendations for improving cost-efficiency and productivity.

The benefits of a materials tracking solution

Overview
Accurate and timely information regarding material tracking and production assets is critical to facilitate leading practice unit cost and resource recovery at mine sites. All levels of a mining organisation can use this information to ensure smart decision-making, disciplined execution of a mine plan, and high production from mobile and fixed equipment fleets.

Typically, the use of data in an operating mine can be broken down into three key areas:
1. Production reporting – frontline production including metrics regardless of location, total tonnes, operating efficiency, tonnes per hour, usage or availability
2. Material tracking – geology including location-based, number of tonnes moved from X to Y
3. Budget forecasting – Mine Planning including longer term production data used to revise assumptions and project performance metrics.

Challenge
The Emeco Operating System (EOS), a pioneering piece of technology used to accurately measure and improve mine site performance, has a proven track-record of delivering improved operational efficiencies for Australian mining companies.

In April 2016, our team installed the EOS at a gold mine site in Western Australia. During initial discussions with the site’s production and geology teams, we identified the system’s potential for materials tracking, including end of month (EOM) reconciliation, minimising data entry by replacing paper-based “plods”, and reporting and performance feedback.

Using the EOS, our team highlighted and addressed limitations to the existing system’s accuracy. These limitations included the communications network, system stability, and user error resulting in either lost loads or incorrect data capture.

With regards to communications network solutions, the following strategies would benefit the site in this case:

  • Deployment of an additional trailer
  • Modification to the aerial brackets, to improve communication between the back haul and radio tower
  • Development of a daily communications performance analysis
  • Liaison with the mine planner and fitter, to adjust aerial orientations as mining progresses
  • Conducting trailer repairs.

User error was another issue which the EOS addressed with some key recommendations:

  • Refresher training packages for operators
  • Feedback to supervisors on incorrect grade/blast/dumping selections
  • Configuration changes to allow operator override and locking options
  • Spare parts stock for critical items to be replaced when damaged.

System stability solutions were also useful in this case, including the migration of servers to the cloud, and the implementation of system health alerts.

Result
Following implementation of the materials tracking solutions mentioned above, the site experienced noticeable improvement across the identified limitation areas. Load reconciliation to survey reached 100 per cent, and ore block allocation achieved 99 per cent accuracy. In addition, materials tracking widgets were developed, and training programs for geologists held to update grade/blasts and system geofences. We also worked with the site to implement a change of management and switch to a paperless system.

Fast Facts: Material tracking performance

Summary
Emeco is dedicated to supporting mines in maximising operational performance. Through our innovative technology and pioneering attitude, we help mining teams avoid complacency by continually looking for opportunities for improvement.

The EOS provides valuable insight for mining projects, with detailed data enabling a thorough site evaluation right down to individual assets, operators or materials. As in this case, mine sites benefit from being able to accurately measure their fleet payload performance, dig rates, operational efficiency and time utilisation. Once this measurement and evaluation is complete, materials tracking and other solutions can be implemented to deliver improved performance and cost efficiencies.

Improving the operational efficiency of site equipment

Overview
To deliver improved operational efficiencies for one of our client projects, the Emeco Operating System (EOS) team investigated increasing the bucket size for their main production digger. The review was primarily conducted to assist in achieving the project’s increased annual mine plan targets, but also resulted in significant potential savings.

Challenge
By increasing the bucket size of the EX2600 digger there was a risk that the bucket weight could be overloaded making it unstable to operate and resulting in damage to the machine. The situation therefore called for a review of the machine specifications and some careful mathematics.

The current bucket capacity of the EX2600 digger is 15m3. On site, this machine historically achieved a bucket fill factor of less than 80 per cent. Machine specifications indicate the bucket has a maximum lifting capacity of 38.4t, based on a maximum loading radius of 12m and a lifting height of 4m above the bench, which is appropriate for a 100t truck. The 38.4t lifting capacity utilises 87 per cent of the full machine’s hydraulic capacity and could not be exceeded

To upgrade to a standard 17m3 bucket, weighing 15.6t and allowing 4.4t for bucket armouring, gives a maximum bucket payload of 34.4t. If the fill factor were to increase to 95 per cent, it would reduce the total bucket capacity to 16.15m3. To overload the machine, a material density of 2.13t/m3 would be required. But the client indicated that material of this density was unlikely at the project.

EX2600 Digger Fast Facts

Results
With the current fleet, re-fitting the original 17m3 bucket to the machine posed no significant risk to the asset, unless loose density of material regularly exceeded 2.1t/m3 with a fill factor of 95 per cent. Prior to fitting the bucket, a loose density study was conducted to confirm fresh rock above 2.1t/m3 was unlikely to occur.

With a spare 17m3 bucket on site, the switch was calculated to cost the client only $6,000. Our team’s in-depth cost modelling revealed the $6,000 spend would also result in a potential annual cost reduction of $220,000, or $500,000 in flow-on benefits.

The optimised scenario was subsequently executed on site, increasing the bucket capacity by 2m3 (from 15m3 to 17m3) by removing the reducing plates and re-fitting the original.

Summary
To continually achieve rising targets and improve efficiencies, it is important for mine sites to avoid complacency and seek new ways of doing things. We are committed to supporting our clients by presenting innovative solutions, identifying operational inefficiencies and recommending equipment modifications that will maximise production and profitability. In this case, the EOS team highlighted a low-risk strategy that improved the performance and efficiency of one of the site’s most important pieces of equipment as well as resulting in significant potential savings.

Will a fleet upgrade help you achieve TMM targets?

Overview
At Emeco, we believe complacency is a barrier to success in business, particularly when it comes to achieving productivity at mine sites. Requirements often change on site, which means the ability to adapt and overcome challenges is critical.

When it comes to upgrading equipment, measuring and benchmarking performance accurately can make a world of difference, ensuring any changes to existing fleets are going to have the desired impact. As a leader and pioneer in innovation and mining equipment solutions, Emeco offers mine sites the best in performance evaluation through its fleet management and mining technology platform the Emeco Operating System (EOS).

Challenge
The Mungari Mine in Western Australia had correctly selected a truck and shovel fleet on site to achieve the mine’s plan for total material movement (TMM) in XXXX. To achieve increased production for the following year, an additional 777 trucks were provided to the site.

Emeco’s EOS was used to evaluate Mungari Mine’s future mine plan requirements. The EOS allowed us to identify production constraints based on the performance of the existing truck and excavator combination, giving us a clear picture of potential setbacks and obstacles.

Results
We benchmarked current performance against mine plan targets, to assess whether TMM would be achievable if production efficiency improved. The results showed that even if industry-leading performance was achieved with the current excavator and truck fleet, TMM would still not be reached.

The team at Emeco created an opportunity model to analyse what options were available to us to meet production requirements for the mine. This model identified that not only would TMM be reached with an additional 785 fleet, but unit costs would also be reduced with improved production efficiency.

Following this recommendation, we sourced a 785 fleet from across Australia for the site. The result was very satisfying. By using the EOS, we improved production efficiency helping the site to achieve its required TMM.

Summary
The EOS is a vital tool for sites wanting to better manage their production operations and reduce mining costs. In this case, our innovative approach and leadership demonstrated through the EOS, we helped the Mungari Mine meet its evolving TMM targets without increasing unit costs.

Fast Facts

  • Current EX2600 digger and 777 truck fleet = $1.10/t mining cost
  • Proposed single shift with PC2000 digger and 777 truck fleet, and double shift with EX2600 and 785 truck fleet = $1.07/t mining cost.

Equipment servicing: are you using the right scheduling method?

Equipment servicing: Are you using the right scheduling method?

Overview
Scheduled servicing contributes significantly to a site’s costs, downtime and maintenance labour requirements. Typically categorised into A, B and C classes, scheduled servicing generally occurs at 250, 500, 750 or 1000-hour intervals for mobile equipment.

The focus of scheduled services is often on increasing the lifespan of major engine components. However, due to the frequency and expense of these activities, downtime and labour can end up costing more than the engine.

To simplify maintenance scheduling, services can be switched to occur on a calendar rotation.  For example, a 500-hour service could be scheduled to occur monthly. This allows a simpler scheduling regime compared to maintenance of a machine based on hours, as well as a more effective use of workshop space and maintenance labour.  However, calendar-based scheduling can come at a high cost – one that is often not truly appreciated.

Challenge
A mine that runs its scheduled servicing based on machine hours has its maintenance team wait until the asset accrues enough hours to be ‘in the window’ to then execute a service.  Shown below is a distribution of the engine hours between scheduled services for a mine running 793D trucks.  There is variance, but most services are conducted between 400 and 500 hours, with the average being 450.

Results
Emeco conducted a review of the mine after changing its scheduling regime to services based on a calendar drumbeat.  It was a great opportunity to see the same team, with the same assets, using a different maintenance approach. The below graph shows what happened.

The variation of hours between services spread out considerably, with the average time between services dropping to 253 hours; effectively doubling the serving levels on the assets.  It is worth noting however, that some assets in the fleet remained at a service interval of 500 – 540 hours.

The distribution is spread because the actual utilisation varies significantly through the fleet.  Actual usage depends on many things, and any given calendar-based schedule chosen would result in the same.  It is not a practical option to extend the time between services, as it would mean high-utilisation assets would have a much higher time between service, putting the asset at risk.

Summary
Moving to calendar-based scheduled services can simplify labour management, improve workshop space and increase work management metrics like schedule compliance.  However, there is a high price to pay for that simplicity – which in this case was more than 1,344 direct labour hours per annum. This isn’t unusual, and is typically the case for trucks and ancillary equipment.

However, for assets that don’t have a high variance in monthly usage, the calendar system can work well. For example, front line excavators often have a consistently high utilisation, which makes calendar-based servicing more efficient.

Making the best choice
Many sites become complacent about the issue of scheduled servicing, and accept the costs as part of doing business. At Emeco, it’s our mission to add value for clients by fostering continuous improvement on equipment and service solutions.  We make smart asset management decisions to maintain equipment availability and reliability in the most efficient way.

For maximum efficiency and profitability, it is worth considering your site’s equipment usage (by the hour) and servicing frequency. This will help you identify the best method for minimising costs while maintaining the health and lifespan of site equipment.

Emeco, which provides safe, reliable and maintained equipment to the global mining industry, has challenged this standard practice to find a more efficient method.

Fast facts
Calendar-based scheduling

  • Reduced to 253 hours on average between services
  • More than 28 trucks per annum = 1,344 direct hours
  • At 45 per cent tool time = 1 additional fitter required for the 793C fleet
  • Hire truck value of $350/hour = $470,000 hire cost per annum.