New technology on a large asset tends to be judged by its ability to deliver operational efficiency gains.
But when an exciting innovation comes along, operators will often take a calculated risk and invest in its potential, even if the technology lacks the validation to guarantee benefits in a defined timeframe.
Digital twins are a case in point. Everyone wanted a digital twin to headline their digital strategy, and many operators paid top dollar on the basis of visually impressive 3d demos that had never been piloted before on a real asset.
Two or three years down the line, some of those operators complain to me that they’re still waiting to see the measurable, real world efficiency gains they were expecting.
Well, in my experience, there’s usually a good reason why these so-called digital twins are failing to deliver. It comes down to the gulf between what the operator thought they were buying, and what they actually got.
3d model vs a digital twin
3d models and digital twins are easily confused because they look similar at first glance. In both cases. what you see on the screen is a detailed visualisation of your physical asset in three dimensions. The difference – and it’s a big one – is the data that appears on the 3d model.
A barebones 3d model without data has some use as a point of reference on a sprawling gas plant or oil rig. It basically does the same job as a traditional paper plan by providing orientation on the asset. With more advanced 3d visualisations, you might get some additional static data attached to the model. Static here means that the data cannot be updated easily. An example would be a pdf document containing an equipment spec sheet.
Theory meets reality
Now in theory static data ought to help people make better decisions and complete tasks more efficiently. For example, you might be able to locate a piece of equipment on the 3d model and see its id number on the attached pdf. Armed with this identifier you can then chase up back office to investigate when it was installed, who installed it, and when it needs to be replaced. This is clearly more useful than a dog-eared paper map.
But is it going to transform efficiency and safety on the asset? Is it going to get anyone excited enough to spread the word among their fellow workers? Are shareholders going to be congratulating themselves for spending all that money on M&T because of the impressive ROI?
In my experience, the answer is no. 3d models populated by static data simply aren’t that useful, particularly in operations. Quite often the data is out of date because busy staff and contractors have other priorities and don’t really see the point in spending time and energy on curating the model. In their eyes it’s just another digital initiative that adds to their workload.
Because the data isn’t always updated, it isn’t 100% trusted. Because it isn’t trusted, people don’t bother to consult the 3d model. The net result is that people in operations quietly revert to their old ‘tried-and-tested’ processes.
Which leaves your digital strategy stuck in the mud.
The (huge) difference between the 3d model and a digital twin can be expressed in two words: dynamic data.
Unlike static data, dynamic data changes and updates in real-time on the 3d model as soon as new information becomes available. One example would be a sensor on a pipe that shows real-time temperature or pressure measurements on the 3d model, and is able to trigger an alert. From the point of view of someone on site with a maintenance task, this is extremely useful information that makes their job safer and more efficient.
If that alert can be actioned promptly before a small problem becomes a big one, big savings can be achieved. This is the sort of thing digital workflows are for. They carry real time data insights from the central data repository to where they’re needed in operations, and carry back updates once a task has been completed. In this way the 3d model is constantly being updated with the best and most reliable information that everyone else on the asset can see.
With dynamic data running through its workflows, the digital twin suddenly comes to life. The lights come on and people start using it, because it is making everybody’s lives easier, safer and more productive. With reliable data at their fingertips in one place, people can ‘connect the dots’ to solve problems that previously eluded them.
It doesn’t stop there. As confidence in the digital twin grows, the culture of the organisation becomes ripe for improvement and change. Enlightened operators grab the opportunity to re-organise staff into multi-disciplinary teams and experiment with new approaches. Given the freedom to solve problems in new ways, staff start gathering around their mobile devices and collaborating. I’ve seen this happen with my own eyes, and it’s a sure sign that innovation is on the way.
The truth is that 3d models incorrectly labelled as digital twins have often disappointed operators, which has unfairly damaged the reputation of this transformative technology. But has the money spent on 3d models been entirely wasted? Fortunately not.
The good news is that your existing 3d model can be upgraded to channel dynamic data by following a few logical steps.
First, you need to establish a central data repository that cleans and harmonises the data so that it can be accessed. Second, you need to install digital workflows. These act as the ‘wiring’ that carry data insights to where they’re needed in operations. Third, you need to give your workforce the chance to understand how useful a digital twin can be in their daily lives. This usually happens by allowing them to experiment with the technology in a safe space.
All of this can be achieved by adopting a low risk piloted approach to digitalisation such as the one developed by Silverhorse. It will breathe life into your digital strategy and create a dynamic digital twin that can be scaled out across multiple assets to create compound benefits.