Article 5: Production Intelligence

For the mining industry to meet dramatically increasing global demand for diminishing, often lower-grade mineral resources as well as many other challenges — including ongoing climate change and potential de-globalization — it is going to need to make use of every single scrap of data it can acquire. Because in data there is knowledge. And knowledge leads to better decision-making, which leads to higher productivity and increased profits.

Properly handling the rising tide of big mining data is not easy, however. As I said in Article 1 of this series, mining professionals today are inundated with so much data ­— geoscientific data, asset condition data, operational data — that they simply don’t know what to do with it all and often do not have the tools they need to analyse it.

In this series of articles, I’ve talked about how big data management (BDM) that includes big data analysis (BDA) is the way forward for the mining industry as a whole, along with the specific challenges of BDM/BDA during the exploration and geology/resource estimation phases of a new mine, and in completing block caving projects. Here, in the final article of the series, I discuss BDM/BDA in mine production.

Production data challenges

Production teams today are inundated with large amounts of continuous, real-time production data, including ore quality information, which determines that the correct feed grade is going to the plant, and stockpile volumes that must be continually re-calibrated as ore is added or removed.

In addition, many production teams currently find it difficult to visualise and monitor material flow, which is critical to determining an efficient mining process. And some are lagging behind in measuring equipment performance, still using a basic Time Allocation Model (TAM) where mining engineers gather data related to equipment hours. The engineers then allocate that data to measurement groups — usually Operating, Delay, Standby, Downtime, Planned Maintenance and Unplanned Maintenance — that, added together, equal total Calendar Time. From there, many manually create a simple spreadsheet, like this:

At a time when efficiency and fuel consumption are major KPIs to demonstrate a mine is operating sustainably with the smallest possible carbon footprint, this is simply not good enough. A chart like this does not promote more detailed data analysis or make it easy to compare current results with historical performance.

In addition, mines today need to be able to include both fleet management and equipment operator data so that they can record and analyse all haulage equipment and operator activities, and not only connect each movement with the associated loading and haulage equipment but also with the operator of the equipment.

With that information at hand, mines can then uncover the patterns in equipment performance and detect important issues, such as carry back (material stuck in the tray after unloading, reducing the tray capacity), maintenance issues with a particular truck fleet, over-reported truck counts, under-loaded excavators or incorrect excavator loading. They can also identify:

• under-performing crews, supervisors, shifts, excavator and truck operators
• lower-than-expected production or differences in production between shifts
• lower-than-expected excavator production
• incorrect driver positioning of trucks for loading, and
• incorrect recording of truck counts.

One solution

GEOVIA’s Production Intelligence application enables mines to collect, store, and integrate all their production data — no matter the type or source — on the 3DEXPERIENCE platform.

With all production data in one location, production staff can then index that data at source, analyse it, and visualise the results in a variety of ways, including standard dashboard displays that show, for example:

• production actuals against production targets, ore processing metrics, and equipment performance
• stockpile balances and equipment fuel consumption, and
• material flow and associated KPIs.

Production teams also have the option of developing customised dashboards to visualise other aspects of mine production, and can choose to select, adopt, and customise a variety of advanced-analytics programs, including AI and machine learning. Once a data analytics project is underway, they can also:

• create and share a custom analytical page with notes on the initial AI/machine learning analysis

• assign and manage tasks to investigate the analysis, and
• generate a Wiki that puts the contents of the final analysis into context for all current and future team members — a benefit particularly for large production teams that may also be siloed.

There’s more

Once your mine’s data is consolidated and analysed in any of GEOVIA’S four Mining Intelligence apps — Exploration, Geology, Production, and Caving — you will be able to aggregate and publish that data in any form you choose, including spreadsheets, charts, and graphs that can be included in CPR and other reports.

You will also be able to share information about (and generated by) any of your data analytics projects quickly and easily with all stakeholders through a simple, web-style portal. However, while the latest data only appears on your stakeholders’ screens, the system retains all previous versions to ensure your data analytics project is fully traceable and auditable.

In the current mining environment, it is becoming increasingly vital that mines use all the data at their disposal in order not just to maintain the status quo, but to get ahead in a competitive industry.

Mining Intelligence is the way to get ahead. For more information, please contact me or email GEOVIA.info@3ds.com.