McKinsey uses cookies to improve site functionality, provide you with a better browsing experience, and to enable our partners to advertise to you. Detailed information on the use of cookies on this Site, and how you can decline them, is provided in our cookie policy. By using this Site or clicking on "OK", you consent to the use of cookies.

Back to Insights

MPI Analysis

Measure the productivity of your mining operations



Boosting process stability and output

2018


Coal is by far the highest volume mined commodity, but to date, the coal industry has not taken a lead in embracing the new approaches being made possible by digital advances. Coal companies now have large amounts of data generated on the mine site. There is an important opportunity for the coal industry to benefit from the ways that other parts of the mining industry are already using this kind of data to make mining processes run more consistently with improved process stability and to boost output. The gains from these approaches can be substantial. One large opencast mine was able to raise its overall productivity by 25% over a two year period, and now far exceeds top quartile mining industry performance worldwide on truck and shovel output (as measured by McKinsey’s MineLens Productivity Index, which benchmarks mine performance across 250 mines worldwide). The investment required to cover data capture, analytics and workforce training is in the range of US$1 - 3 million, which is low relative to the impact typically achieved; initial benefits can be captured within three months.

Coal mining’s productivity pain point: lack of process stability
It is well recognised in coal mining, as in mining overall, that achieving stable operations is one of the industry’s greatest challenges. This lack of stability is a major contributor to the way that mining lags behind many other industries on measures of productivity performance. Mines can have very good days when all the systems are working together and there are no equipment breakdowns, but they typically struggle to consistently perform at this level (Figure 1). The consequences of this lack of stability are familiar to coal mine management. It causes inaccurate planning and suboptimal scheduling, which – at the mine face – can lead to excessive drilled inventory that is liable to collapse, Nathan Flesher (USA) and Eben van Niekerk (Australia), McKinsey & Company, describe a data analytics-based approach to improve the stability of coal mining processes and raise productivity. spontaneous combustion of coal stockpiles and excess fleet capacity. Further down the product delivery chain, unstable operations make it necessary to hold higher volumes of coal in stockpiles to ensure supply is always on hand, and to carry excess buffer supply at wash plants


For full article please download PDF