The importance of machine learning in the mining industry
During the early 21st century, many commodities prices including minerals significantly rose due to the large growth of emerging markets. During this phase, all mining companies were solely focused on increasing their production, without worrying much about their productivity and costs. That’s why when prices started falling in 2014, many companies were not prepared and had to make strong moves to increase their productivity.  I was able to experience this cost-cutting sense of urgency while working for multiple mines in South America.
After downsizing the overhead and negotiating contracts with suppliers, the only way to reduce yours costs is to work on the productivity of your processes. The mining industry produces a significant amount of data at multiple levels of their processes, however much of it is very complex and difficult to analyze on real time. Using machine learning algorithms, makes it possible to take decisions with the data without delaying the processes. 
Finally, given the scale of mining operations and the high cost mining equipment, even increasing productivity or utilization on a small degree can have a high impact on financial performance of the company.
Machine learning innovation on BHP Billiton
The mining macro processes are shown on graph 1. Most of the machine learning applications that BHP Billiton is working on for the short and medium term are concentrated in the extraction, processing and transportation of the minerals, mainly because they involve a higher number of repetitive activities. 
In mineral extraction, BHP is working on two fronts: self-driving trucks and automated drills. The company is implementing fifteen automated trucks in an iron ore mine in Australia. Automating trucks inside a mine has benefits in terms of preventing accidents caused by sleepy drivers, decreasing variability among performance of different vehicles and improving vehicle utilization, therefore maximizing output. The use of autonomous drills to replace the traditional manual drills has helped BHP improve its productivity by 16%. Drills are used in mining to dig holes to insert explosives for the blasting, which allows shovels and trucks to remove all the material. Also, the drills can test how hard or soft the ground is, so that later they can precisely estimate the quantity of explosives required. 
In the minerals processing stage, BHP is introducing a system to automate the decision-making process of choosing what crushers better fits the batch of materials is being transported in a truck. This allows them to reduce costs and idle time, therefore improving its crushers utilization and output. 
Finally, BHP is also working to automate its logistics and transportation of the minerals. Typically, ores are transported from by train to a port, and then loaded into vessels. Transportation in many operations can easily become the bottleneck, and for that reason, optimizing utilization and capacity is a priority, which is achieved by having autonomous trains and an autonomous loading process to move ores from the train into a vessel. 
Is the company doing enough?
Even though BHP is innovating and finding ways to apply machine learning along all the value chain, there are still multiple areas where it could dive deeper. The two areas where I believe there could be more impact is on mineral processing and maintenance.
After the ores are extracted from the mines, they must go through different physical and chemical processes to concentrate the metals they are looking to harvest. These processes are usually operated under parameters based on historical data and estimations, but given the number of sensors and real-time data, BHP could start using machine learning algorithms to optimize on real time the parameters of their processing plants and obtain higher recovery rates.
The maintenance department could also take significant advantage of applying machine learning algorithms. Reactive and preventive maintenance are an important source of waste for equipment utilization. While reactive maintenance can abruptly stop your extraction or processing lines, preventive maintenance when it’s not required can also decrease your equipment utilization and output rate. By levering the sensors of the vehicles and other equipment, and applying machine learning algorithms, BHP could apply predictive maintenance, which would make them park their machines only when it is absolutely required.
Will technology be enough to sustain profitability in this declining margins industry? Will there be social consequences of cutting down labor due to machine learning in an industry that already employs few people and that many times has a negative perception among the local communities? These are some of the questions that time will solve.
[Figure 2] Komatsu Ltd, “Autonomous mining dump truck Komatsu IAHV”, gruzovikpress.ru, Feb 2018, http://www.gruzovikpress.ru/article/15015-avtonomniy-karerniy-samosval-komatsu-iahv-tochnoe-predskazanie-kontseptsii/
 EY, “Productivity in mining: now comes the hard part”, ey.com, 2018
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 Jurgens D. (BHP Billiton CTO), “Creating the future of mining – integration and automation”, bhp.com, 01 Nov 2017, https://www.bhp.com/media-and-insights/reports-and-presentations/2017/10/creating-the-future-of-mining-integration-and-automation
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