MCKINSEY: Mining companies need to get on the data train too

an old coal train. Kevin Frayer/Getty

The potential economic impact of mining companies embracing big data is $500 billion by 2025, according to a new McKinsey report.

The productivity of global mining operations is falling rapidly, down 28% on a decade ago.

The development of new mines is slowing and existing mines are maturing. This has resulted in the production of lower-quality ore, from further within the mines.

Mining companies need to achieve similar equipment effectiveness rates as the oil and steel refining industries, which stand at 92% and 90% respectively, says McKinsey. By comparison, the overall equipment effectiveness of underground mining is 27% and 39% for open-pit mining.

The mining companies already produce a lot of data through the sensors placed in their equipment and around mines. The data could be better analysed to drive geological modelling, daily operations scheduling, increased meachanisation, managing hazardous conditions, and predictive maintenance.

A better understanding of the resource base could optimise drill and blast patterns, reducing the amount of wastage.

Algorithms and mechanisation can efficiently coordinate the flow of machines and product, matching trucks to loads. This has already been shown to improve productivity by up to 10% in some mines.

Real time monitoring and decision making, through a centralised system, would allow a mining company to match actual results against projections, and immediately respond.

What’s more, there is a virtuous cycle to the use of all this data. As more data is analysed and used, the algorithms and machines will become more effective. Equipment will last longer and interact better with humans, workplaces will be safer and productivity will increase.

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