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Why Industry 4.0 needs AI + IoT


AI and IoT technology needs to be combined to fuel the Industry 4.0 revolution.


IoT and AI technology are often talked about on their own or without context to the other. Frequently, they’re two sides of the same coin.

To simply dissect the two:


IoT: IoT or IIoT (Industrial Internet of Things) technology is about enabling miners to collect vast quantities of real-time data about their operations via infield sensors, through links from OT and IT, as well as via third party resources providing relevant information (such as weather). This generally goes into a platform through which it is combined and can be examined via dashboards to assess performance i.e. real-time asset, plant and personnel monitoring.

IoT in itself presents miners with opportunities for transformation and improvement, removing the need for people to enter hazardous environments to take measurements and facilitating the collection of minute-by-minute accurate readings so that productivity rates can be maintained. Whilst this is transformation, it’s questionable whether it can be classified as Industry 4.0 - it's probably closer to 3.5 - as it lacks the critical decision-making and analysis required to meet the definition.

AI: The AI element of an Industry 4.0 solution is about the applications and subsequent actions taken (whether by human or via automation based on pre-programmed business logic) as a result of the use of machine learning algorithms being applied to data sets.

Once you have in place a way to record all of the potential variables that go into an operation, it’s possible to design and deploy machine learning algorithms that analyze them all within the context of the other data sets to determine:

If the data is accurate (and if it’s not, what the values should be)

What data is actually impacting performance

Predict the outcomes of changes to operations or third-party variables (e.g. impact of rain)

Find optimal ways of achieving objectives.

Over time (and with growing data sets) machine learning algorithms find new and more effective ways to operate - they get better - and multiple ones can be applied to the same data to experiment with new use cases or test alternative hypotheses and optimizations (via digital twins).

Technological advances have enabled the data that AI needs to function to be collected in real-time (usually with a critical IoT component) and so platforms have developed that can apply machine learning algorithms to this data to give immediate insights and direct improvements or action based on programmed objectives, e.g. preventing accidents or ensuring plant uptime etc.

On its own, machine learning can be applied to historical data sets to find optimizations, but it is its ability to use it on real-time data sets that makes it Industry 4.0 as, once there is trust in an algorithm, it can be enabled to exert real-time control via automation. This is what is at the heart of what people mean by AI: machine learning-led automated interventions that self-optimize over time on an ongoing basis.

I4 Mining is a sustainability technology provider to the mining sector, offering ready-to-deploy ESG solutions that help miners to succeed both strategically and operationally without ever getting in the way of good business.

Our solutions enable you to easily develop strategies, deliver accurate sustainability metrics and reports in real-time, improve sustainability and business performance, as well as make predictive analytics and forecasts part of your everyday so that you can reduce risks and optimise from mine-to-market.

Speak to us today to book a demo and discover how you can get started on your digital sustainability journey.


Jared Oken

CEO @ Rayven


Want to know about industrial AI + IoT more broadly?


If you'd like to find out more about the technology that underpins all of our digital mining solutions, other industrial uses of AI + IoT, or are eager to get into the detail of precisely what AI and IoT technology are then visit the Rayven blog.

Visit the Rayven Blog.