How automated tag management helps deliver digitalisation
The digital twin, with its advanced analytics, visualisations and advanced communications technology, is expected to provide seamless access to trusted, fail-safe data supported by relevant documentation to operations and maintenance teams wherever they are based.
In the best-case scenario, a digital twin significantly increases operational efficiency, while reducing HSE and compliance risk. Operations teams spend less time searching for content and can instead focus on value added engineering tasks. So engineering firms put in expensive and laborious processes for compiling a 3D digital model, that incorporates varying degrees of design and operational data, before project handover to the client.
But, if that is all it is, then what they hand over isn't a digital twin. It's an exercise in cartography.
The map is a snapshot of a moment in time. It can be a useful navigational aid, but it is not a real-time representation of real-world topography. Such a map cannot drive greater efficiency and safety into the operation of the asset. A digital twin is supposed to be living and dynamic. It is, in fact, closer to a 4D model with time – and the changes it brings – being the crucial element that manual processes and basic automations cannot capture.
An obvious question then, is how the engineering company can offer an evolving digital record of the asset it has designed and delivered, as well as its ongoing operations, once its team have handed over the keys and stepped back from a completed project.
This is where automated tag management comes in.
For a digital twin to be fully useful, it needs to be 'tagged'. In other words, every little component or system needs to have a tag attached that associates it with the relevant technical documentation, operational history, maintenance information and all the rest.
Traditionally, tagging has been done manually, or subcontracted to a third party to do manually. It is an immense job, whoever does it, and it adds huge amounts of time and expense to a project. The sheer volume of asset documents and data to maintain can be overwhelming. And digital twins have been underachieving as a consequence.
Automated tag management replaces these severely sub-optimal manual processes – and eliminates the problems associated with it. As the name suggests, it automatically scrapes all the relevant tags associated with the asset, and then automatically assigns them to the right data and documentation.
It considerably streamlines the task of creating and maintaining the digital twin by providing the solid foundations on which the digital twin is built.
Asset owners have reported that the amount of time members of staff in operations and maintenance side spend on locating necessary documents has been reduced by 50 percent, because they are no longer chasing down missing or incorrect tag data.
Those achievements are substantial in their own right. But if we pan out, we can see there is even more at stake here. There is now a record of failed initiatives and companies wasting millions on projects that have been underwhelming at best. If digital twins and related digitalisation projects continue to underdeliver, it becomes a barrier to further investment and risks stunting progress of a very necessary digital transformation in industrial sectors around the world.