November 04, 2016

Robotics and automation will reduce mining employment by about 50% by 2030

Economist, lawyers and sustainable investment studies at the International Institute for Sustainable Development have a paper that looks at the mining industry. They look at how automation will effect mining jobs.

Given the fundamental uncertainty and longterm nature of automation technologies, we do not focus on them in this study, instead assessing new technologies that arebeing piloted today, which will be carried forward in the near-to-medium term. These technologies include:

1. Autonomous haul trucks and loaders: One person alone can already remotely operate a small fleet of these autonomous trucks. Improvements in software are likely to allow this to be performed even more efficiently by algorithm-driven computer programs. Driverless technology can lead to a 15 to 20 per cent increase in output, a 10 to 15 per cent decrease in fuel consumption and an 8 per cent decrease in maintenance costs.






2. Autonomous long-distance haul trains: Technologies are being piloted that allow long-haul trains carrying bulk commodities to run fully automated from the mine site to the port.

3. Tele-remote ship-loaders: Fitted with video cameras, thermal imagers, lasers and sensors, tele-remote shiploaders are operated from a control room with a line-of-sight view. This type of automotive technology is unlikely to have an adverse net impact on employment, given that the operator is just moved from the cabin of the ship-loader to the control room, but the skill set changes.

4. Semi-autonomous crushers, rock breakers and shovel swings: These machines reduce the size of large rocks and scoop up the ore at the location of extraction. The mobile crusher performs two tasks simultaneously as it transfers the crushed rock directly for processing via conveyors, eliminating the need for haul trucks within a mine.

5. Automated drilling and tunnel-boring systems: These are used in open-pit mining and exploration activities. One operator can monitor up to five machines from a remote monitoring station. The remote operator needs only an interface with the machine to tell in what order the drill pattern should be drilled. The tunnel-boring machines significantly reduce the time, cost and risks involved to build and expand an underground mine. They are likely to halve the number of contractors involved in drilling and blasting, and those required during the construction phase.

6. Automated long-wall plough and shearers: This technology is being implemented in the coal mining sector. Before automation, workers manned the long-wall roof supports on hydraulic jacks, called shields. Similar to the automation of blast-hole drills, remote operation keeps workers out of harm’s way near the drills and potential falling debris.





7. Geographic information systems (GIS) and Global Positioning Systems: GIS is now commonly used in almost all aspects of mining, from initial exploration to geological analysis, production, sustainability and regulatory compliance. Over time, however, as the use of GIS becomes more evenly dispersed on a global scale, old procedures for mine surveying will become redundant. Automated positioning systems can manage and improve the safe operation of heavy equipment such as dozers, drills, excavators, loaders, scrapers, graders, soil compactors, off road trucks and light vehicles.

8. Autonomous equipment monitoring: Using many different technologies, from cameras and thermal imaging to self-aware machinery able to report its progress, equipment monitoring is extremely important, as preventive maintenance workers can make up a large proportion of the workforce on a mine site.

9. Programmable logic controllers (PLCs): Flexible PLCs are digital computers that typically automate industrial electromechanical processes and replace relays, timers, counters and sequencers. They are an enabling tool for improved process control. Once installed, they can be reprogrammed to improve the control of processes across the full spectrum of industry activity. This technology is the most crucial in the automation revolution and arguably the most important in taking away semi-skilled onsite jobs.

10. Control systems: Offsite control rooms are becoming bigger and more complex as mines become automated. Today, only mining companies with the most advanced technology have control systems that employ a substantial number of workers.

It is difficult to predict the speed at which these technologies will be rolled out, but the automation literature suggests that we are entering an era in which the availability of automation technology is accelerating rapidly. The technologies described above, all of which are in use now, are likely to reach their peak rates of deployment in the next 10 to 15 years. The commodity price downturn seems to have accelerated the move towards automation as companies are looking to increase mining productivity while reducing spending on staff, capital and energy.

New technology will change the nature of mining personnel tasks, whereby workers become passive supervisors of the process rather than active operators of equipment. Automation will reduce the number of operational jobs in areas such as drilling, blasting, and train and truck driving—areas that typically constitute over 70 per cent of employment in mines.

New roles will be created in the development, observation, servicing and maintenance of remotely controlled autonomous equipment as well in data processing and systems and process analysis. Consequently, workers with specialized skills in remotely controlled and automated systems will be in demand as automation increases, while current employees will need retraining, re-education or both to keep their jobs.

Both case study firms are mining entities, with some basic processing (concentration), and both have total annual expenditures exceeding USD 600 million.

The distinction between local and international procurement is worth noting.

For the high-income OECD country mines, local procurement amounted to 58 per cent of total operational expenditures. The lower-middle-income country operation, by contrast, procured only 12 per cent of its goods and services within the host country.

While there is not enough research into the impacts of automation on the size of the mining workforce, they used two
broad estimates on which to base our scenarios. McNab et al. (2013) suggest that introducing fully autonomous equipment “would reduce the workforce of a typical open-cut, iron-ore mine by approximately 30 to 40 per cent.”

In another report, Accenture (2010) evaluates the economics of three types of equipment (trucks, dozers and drills), suggesting that automation could reduce the number of operators in open pit mines by up to 75 per cent. The IISD built three scenarios of workforce reduction through automation, bracketing the two estimates cited above with rates of 30, 50 and 70 per cent. Doing so involves going through the detailed procurement accounts for each firm and deciding which would be sensitive to changes in workforce size.

Beyond the impacts of workforce reduction, we noted above that driverless technology using equipment under control (EUCs) will have impacts by saving on fuel consumption. Spence (2014) estimates that driverless technology can bring about a 10 to 15 per cent decrease in fuel consumption. They assume a 12.5 per cent decrease in diesel consumption in our scenarios. In addition, assuming that haul trucks in the mine are using EUCs and using the rate calculated by Fortescue in its iron mines in Australia, an extra 2.3 per cent in fuel savings could be achieved if they are installed in trucks (Australian Government, Department of Resources, Energy and Tourism, 2014). As such, they assume that mines could save 14.8 per cent in fuel used for hauling if they implement autonomous systems and EUCs. They also assume that 50 per cent of diesel in our sites is dedicated to hauling (a conservative estimate), and therefore subject to reduced demand under automation.

Another way of assessing the impacts of mining expenditure is by measuring the number of jobs created.
Direct employment in the high-income OECD country was 1,457 jobs, of which all were domestic. They estimate this number increases to 4,801 when indirect and induced effects are considered. In the lower middle-income country, the corresponding figures are 2,550 jobs, of which 2,470 are domestic, and 5,100 jobs if indirect effects are considered.

These baseline case figures for jobs created can be modified using the three scenarios for job loss to get a picture
of the types of impacts automation might have in terms of employment. Only direct and indirect effects are
considered; if induced effects were included the numbers would be higher.
• Job loss in the high-income OECD country ranges from 1,016 to 2,372.
• Job loss in the lower middle-income country ranges from 1,530 to 3,570.

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