AI, machine-learning and advanced analytics company N4mative finds that many mining companies are experiencing significant business challenges, particularly regarding master data, with many struggling to keep internal master data synchronised with data provided by original-equipment manufacturers (OEMs).

Having analysed the mining sector, the company has designed business-focused AI and data solutions which include custom data infrastructure, machine learning models and digital workflow tools that will accelerate the rate at which N4mative can deploy its solution for a client, lower operational costs and build operational efficiencies.

It started with direct technical architecture development and data integration for the mining sector, providing enterprise data and IT architecture services for a cobalt and copper mining business active in Africa.

“We have built a product that has a wide range of capabilities. When we engage with a client, we first showcase those capabilities and then tailor the solution to that specific mine’s operations,” says N4mative CEO Kevin Gungiah.

Data captured through the N4mative platform is combined with a mine’s operational datasets and relevant OEM-generated information. This creates a structured data foundation from which customised analytics, models and decision-support workflows can be deployed.

N4mative has applied these capabilities to address challenges including inaccurate procurement information, maintenance delays, operational downtime, fragmented workflows and avoidable operating costs, he says.

“Instead of having three or four separate systems managing inventory, asset maintenance, procurement and operational workflows on site, clients can use one connected decision-support environment,” adds Gungiah.

As a result, the platform will help to prevent situations where employees are constantly switching between disconnected systems to complete a task.

Consequently, better efficiencies are created while connecting a lot more data across business units, rather than solving isolated issues using individual departments.

N4mative business development and marketing consultant Sash Nair says the mining industry’s challenge is not necessarily a shortage of data, but a shortage of connected, decision-ready information.

“Mining operations already generate vast volumes of information through equipment, OEM platforms, sensors, maintenance systems, procurement processes and operational teams. However, much of that information remains fragmented or is only reviewed after an event has occurred.

“The opportunity [for N4mative] is to organise it into a shared intelligence layer that allows leadership to identify risk earlier, coordinate decisions across the operation and deploy people, equipment and capital more effectively,” she adds.

Strengthening Digital Security

Using AI-powered situation analysis and predictive threat detection enables mining companies to strengthen the security of their digital infrastructure and operational technology environment in two ways, says Gungiah.

Firstly, a situation analysis involves assessing operational conditions using signals received from cameras, drones, sensors and other connected data sources through the platform, enabling the platform to generate information about conditions at a specific operation.

These use cases are supported by data from other sensors, enabling the customised AI platform to generate insights that enable mines to deploy personnel and equipment proactively and predictively where they are needed most.

The data helps to improve resource management, the monitoring of resource use and the identification of operational inefficiency, adds Gungiah.

Secondly, AI models that are running in the operation for situation analysis purposes, together with N4mative’s platform, enable clients to monitor how AI models are performing, review governance controls and compare model outputs against the intended operational scope and approved outcomes.

Additionally, where model performance changes or new operating conditions emerge, Gungiah says N4mative can return the model to an engineering environment for review and retraining before redeploying it into production.

In terms of cybersecurity, N4mative AI CEO Parag Doshi says the focus is on ensuring that the right people are accessing the right equipment, as well as understanding the location of certain personnel and the information they can access.

“From a technology perspective, security is really a defence-in-depth approach, with security built into every single layer . . . [Mining] data – whether it comes from cameras, sensors, radio-frequency identification on equipment or geofences – should be treated like protected health information or personally identifiable information,” he says.

The platform can support an organisation’s compliance with applicable data-protection requirements, including the General Data Protection Regulation.