Gold mining started more than 5,000 years ago1 and has been historically a conservative industry where miners relied mostly on skills and experience. Currently, most of the gold is produced by industrial giants such as Barrick, the largest gold miner in the world, with more than 150 tons of gold churned out in 20172. Given its scale, global presence and more than 25-year expertise, Barrick is one of the leading companies in terms of technological development – most of the company’s operations are highly automated (see Exhibit 1 for images of automated control over the operations) and the company continues to pursue full process automatization in the coming years. However, the recovery in processing gold ore remains around 86-92%2 meaning that the remaining 10% of the mined gold is being lost during the extraction of gold from the ore. This implies that out of almost 20 mln tons of ore processed annually 2 mln tons are yielding zero output despite the salient costs of mining these 2 mln tons associated with excavating 10 mln tons of rock. Fortunately, there seems to be a way to address this issue with the advance of data storage and machine learning technologies.
Exhibit 1 – examples of Barrick’s control room and available real time data [Source: Barrick public materials3]
Gold processing stage consists of the set of complex mechanical and chemical processes aimed at the extraction of gold from surrounding other materials – usually gold content is only 2-3 gr per ton of ore processed. Every operation (see Exhibit 1 for processing stage illustration) during this extraction process is regulated by multiple control parameters such as mills loading, temperature and pressure in flotation tanks, an intensity of applied reagents (cyanide, sulfites, acids, etc.) and others.
Exhibit 2 – example of gold mining process stages [Source: Barrick public materials3]
Currently, each of these parameters, although automatically monitored and most often remotely controlled, is set by operators who are prone to multiple human errors, biases, and drawbacks comparing to machines, most common of which are:
- linear extrapolation of trends – e.g. if an increase of pressure increases gold output then plant operator tends to increase it to the maximum. In reality, however, the laws of nature are often non-linear and the optimal point could be not at the extreme
- risk aversion – operators are incentivized based on the previously achieved recovery rates and could be disinclined to risk KPI target in favor of slightly more optimal decision with a huge downside threat
- slow and often late, retroactive decision making – it takes weeks for plant employees to assess the resulting output from chosen set of parameters which may be already irrelevant to the ever-changing chemical properties of processed ore
In short, the processing plants employees are overburden with “routine” type decision making which is, however, too difficult to formalize in “if-then” type of algorithms. In turn, machine learning approach to the issue could benefit from the ample amount of available historical data, a constant relationship between control parameters and output variable and opportunity to train algorithm for optimal parameters to reach maximum gold output.
Barrick’s management has already indicated the launch of Barrick Data Factory, but the current actions are aimed primarily at the low hanging fruits from the simple automation fixes and elimination of safety concerns based on the predictive analytics. To source more fresh ideas the company attracts data scientists and organizes annual hackathons. However, the focus of the technological advancement remains is still vague and allocating a special team solely for the processing optimization with machine learning could yield significant results in terms of gold output at minimal investments compared to capex-heavy equipment upgrades and new deposits development. Moreover, machine learning technology employed for the plants’ control could later incorporate not only production optimization but also solve for the minimal costs (power, components, materials, etc) and therefore unlock previously unprofitable processing opportunities.
However, if Barrick chooses to start developing these technologies the company risks to spend funds on unsuccessful R&D while the competitors could later try to copy or buy the winning technology and save money on its development. The risk of the first-mover technological advancement is further exacerbated by unforeseen risks in the implementation, including the processing lines halts, and retraining of the employees that could be costly.
 US Geological Survey “Gold” by Harold Kirkemo, William L. Newman, and Roger P. Ashley https://pubs.usgs.gov/gip/prospect1/goldgip.html
 Barrick’s 2017 Production Report https://barrick.q4cdn.com/788666289/files/quarterly-report/2018/Barrick-Mine-Stats-2017-Q4.pdf
 Barrick’s public presentations and press-releases: “Driving change”, March 5, 2018 https://www.barrick.com/news/news-details/2018/driving-change/default.aspx; Investor Day Presentation, February 5, 2018 https://barrick.q4cdn.com/788666289/files/presentation/2018/Barrick-Investor-Day-2018.pdf
 BCG research, “Mining Value in AI”, October 5, 2017 https://www.bcg.com/publications/2017/metals-mining-value-ai.aspx
 Harvard Business Review Digital Articles, “How to tell if machine learning can solve your business problem”, November 15, 2016