Our client, a large Iron Ore producer was undertaking several initiatives aimed at increasing shareholder value. Some of these initiatives were to be funded by operational cash-flow however the challenge was to improve operational revenue without a significant increase in capital or operational expenditure.
We were engaged to provide technical consulting and expert data analytics to identify further optimisation opportunities for our client that were not discoverable using traditional approaches to business improvement. Using a consultative approach, a differentiated suite of technology and analytical solutions were applied and a unified data model that integrated mining and plant data was developed.
Providing a combination of mining, processing, and mathematical expertise, practical tools that the site operational team could use to capture improvements in value in the plant were developed. Utilising the power of Interlate’s API Library, an ensemble of bespoke machine learning algorithms was used to perform sophisticated analyses to predict outcomes. The predictive intelligence that came from the machine learning based analytics gave the site team actionable insights. This tool was also applied to the stockpile building process and was able to predict ore feed categories of stockpiles to an actionable level of confidence, even with data that it had not previously seen.
These solutions were able to implemented quickly to materially improve productivity, with little or no additional capital expenditure. The optimisation achievable by using this approach was calculated at 3% increase in ore throughput and 2% improvement in product yield equating to an annualised revenue improvement of $57M.