Digital Twins: Honest Realities for Success

Digital Twins - digital replicas of physical assets promise extraordinary benefits: optimised designs, proactive maintenance, and accessible, up-to-date information. Yet, if we’re honest (and we need to be), they often fall short of their potential, especially when transitioning from engineering into operations. Before we can truly leverage Digital Twins (DTs) in meaningful ways, we need to address some uncomfortable truths; primarily, that there's a gap between the digital twin many envision (or have been sold by software vendors) and the one that can realistically be maintained over the long haul.

Who Owns the Data?

One question is often brushed aside amid the enthusiasm: who is responsible for keeping the Digital Twin up-to-date once the project is handed over to operations? This isn’t just a matter of making sure the design team update the DT, it means assigning individuals or teams with the task of importing vendor data and assigning clear ownership of data to operations and maintenance too. Without continuous updates, the twin will quickly devolve into a snapshot of the asset’s lifecycle, reducing its value to a glorified archive.

When a Digital Twin leaves the hands of an Engineering, Procurement, and Construction (EPC) team and enters the operational realm, ownership of the twin’s data becomes critical. If no one takes responsibility for maintaining it, the DT can’t deliver on its promises. In fact, Digital Twins need consistent interfacing with other key systems, such as SAP, Maximo, P&ID and 3D to ensure data integrity.

It’s More Than Just a 3D Model

When Digital Twins are at their best, they integrate 3D models, real-time IoT data, and historical information, making it possible to simulate, monitor, and predict asset performance. However, all too often, the focus is placed on creating an impressive visual model during the design phase, rather than on strategies for long-term usability and updatability.

From a practical standpoint, building a DT is less about creating a virtual replica and more about creating a sustainable model that has pipelines for continuous data flow, and integration into other operational systems. Connectivity with asset management systems like SAP, Maximo, or other CMMS solutions to keep up with the realities of asset degradation, maintenance, and replacement. Without these connections, the DT becomes a static model in a world that requires dynamic, real-time feedback.

The Disconnect Between Talk and Action

There’s a massive disconnect between the theoretical discussions of Digital Twins and their practical application in the field. Industry articles are packed with phrases like "optimise resources", "enhance safety", and "reduce downtime", yet few address the more critical groundwork; committing to the hard work of data governance, ownership, and continuous upkeep. The world where "all your engineering information is at your fingertips" only materialises if there’s a team tasked with making sure that information remains current, complete, and correct. If we’re not willing to admit that this responsibility gap exists (and it does in many places), any talk of DT benefits is just that, talk.

The Cost of Inaction

Not keeping Digital Twins up-to-date is more than just an operational inconvenience. In mining, oil & gas, or chemical plants, where maintenance schedules, safety regulations, and real-time performance data are critical, an out-of-date DT can lead to delayed maintenance, increased risk, and unnecessary shutdowns. If left to stagnate, it could be even worse and mislead engineers or technicians, leading to costly errors or safety risks.

How to Maximise Digital Twin Potential

So, how can we bridge the gap between theory and practice? Here are some recommendations for creating and maintaining Digital Twins that deliver genuine value:

1. Assign Clear Ownership: Define who is responsible for maintaining the DT’s data post-handover. Without clear ownership, the DT is liable to fall out of sync quickly.
2. Ensure Cross-Integration: A Digital Twin should not exist in isolation. Integrate it with other systems, such as ERP (e.g., SAP or IFS) or CMMS (e.g., Maximo), for data flow and management.
3. Data Governance Commitment: Establish data governance policies that dictate the frequency and of updates, the data sources and attributes, ideally embedding this into regular operational workflows.
4. Create Feedback Loops: The data in a Digital Twin must evolve as the physical asset does. Incorporate mechanisms to receive data from IoT sensors, maintenance logs, and user input. These feedback loops can ensure that the twin reflects the current state of the asset.
5. Implement a Practical Strategy: Think beyond impressive visuals and design a Digital Twin that can be practically updated. Tools that make data accessible on mobile devices or tablets are only valuable if the underlying data is reliable and updated regularly too.

Walking the Walk in Digital Twin Implementation

It’s time for the people responsible for designing, building, configuring and maintaining Digital Twins to get real and admit these implementation challenges exist. It’s an opportunity to move forward and ensure these tools live up to their promises. By admitting the flaws, defining ownership, and integrating with real-world operational needs, we can move from a theoretical ideal to a genuinely valuable digital asset.

The true potential of a Digital Twin isn’t realised by just talking the talk, it’s realised by acknowledging and addressing the practicalities of its upkeep and integration into real-world systems.

The gap between the Digital Twin concept and reality isn’t about technology but about the strategy and commitment behind it.