Digital Twins: The missing links to value
Read time: ~3-4 mins
Speaking with a number of vendors, miners and digital advisors in the mining industry, there seems to be a growing theme around the lack of value and adoption from new leading-edge Digital Twins.
Reflecting on conversations, this theme highlights a pattern of gaps in 4 major areas in comparison to the “Typical Mining Digital Twin Process”, see below:
The Typical Mining Digital Twin process (i.e. taking and creating 'all the data and 3d models' and integrating it into a platform so that it can be viewed in a browser) is the basic theory of most digital twins - however, we have seen again and again that this is not enough. The effort and time to wrangle the data, especially disparate and siloed legacy data, to create high fidelity 3d models, pushes the time and cost out to a point where customers cannot experience value for many months or even years. Worse still, they can only begin the journey of adoption and gap finding when all of this work is complete.
The common missing links we see are:
Aligned digital twin strategy: In the hype to get ‘ahead of the curve’, many digital twins have bypassed the stage of aligning the direction and requirements of the digital twins with the needs and abilities of the company. I.e. what are the company goals in the next 3-5 years? How and where do Digital Twins contribute to these? What adjacent digital transformation initiatives will be affected by this, and how can we prevent obstruction but maximise collaboration amongst these projects (e.g. AI/ML, data lakes, inhouse applications)?.
Scope of practical use cases: It is somewhat oxymoronic that the main issue with digital twins can be the same reason people want to purchase one - "we want an all-inclusive model of our site that integrates everything" - but when this happens, it quickly becomes too convoluted and unclear as to what it's purpose is and who is the owner(s). Regardless if the digital twin model is big or small, simple or complex, it MUST align with practical hourly/daily/weekly/monthly processes where workers are better off using it than without. An easy way to check this is to map out step by step what the ‘use case’ (jobs to be done) are prior to the Twin and what they should look like after. If it’s not reducing costs, time or risk across these steps, chances are it’s not a worthwhile investment, and if it involves multiple new steps, then adoption challenges will become present.
Usability & adoption: The phrase “we don’t have a technology problem; we have a human problem” is at the core of this gap. Mining companies typically have a good appetite when it comes to investing in new tech/innovation to advance their mines; however, what tends to happen is an overload of dashboards, alarms and apps, which not only are difficult to interpret but can actually reduce the ability for workers to focus on the right tasks and use said data for productivity gains. In an age where the human attention span and the ability to focus are shrinking, it is becoming essential to design better ways to deliver, digest and action data for the ‘human in the loop’ at our mines. Predictive tools such as AI are going to exacerbate this problem, especially as we ask people to trust “black boxes” with decisions that humans previously made from personal experience.
The phrase “we don’t have a technology problem; we have a human problem” is at the core of this gap
Sustainable data integrity: A Digital Twin becomes an ecosystem in and of itself, relying on the relevant IT/OT data inputs to stay healthy and accurately reflect the physical world, but transforming and juxtaposing these inputs as it is used. Meanwhile, the physical world keeps changing, and physical structures degrade, and many of these changes are not captured with sensors. As such, from the moment a Digital Twin is ‘switched on’, the deviation from the physical world begins as the infinite variables from the real world begin to change from what has been implemented in the ‘virtual world’ (i.e. the Digital Twin). Of course, solid OT connections should help reduce this and retain data integrity; however when it comes to changes in 3D models, red-line updates, changes from maintenance and repairs, corrosion, upgrades, greenfield/brownfield projects, additions of new sensors and systems etc, there can be a compounding amount of change that will slowly but surely lower the data integrity of your digital twin over time. For now, a critical remedy for this is to employ stringent governance across data integrity and management of change, as well as deliberate maintenance and repairs that should be regularly scheduled on your twin. (Another blog to come on this point in more detail).
Ultimately when it comes to ensuring value from Digital Twin investments, we need to realise that Digital Twins are living/breathing ecosystems that exist in parallel to our physical ecosystem, and they both need to be treated as such. If not, we risk wasting time, money and even opportunity costs when investing in our mines for the future.
The Geminum team are always keen to hear more about people's take on digital twins in mining, so if this is relevant and of interest to you, please feel free to reach out for a chat!
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