CREATING AN INFINITE LOOP OF INNOVATION AND LEARNING FOR EVERYONE

Once created and deployed, you can use digital twins to create self-learning systems capable of optimising everything from energy consumption to maintenance scheduling – ensuring BIM standards are continuously upheld for updated and new data, while improving project value.

However, acting on new insights and improving assets and projects across their lifecycle isn’t always easy. Despite the BIM process aiming to address the issue, often the departments tasked with designing and constructing assets are disconnected from those focused on improving them, making it difficult to secure the financial and physical resources needed to drive positive change.

Fortunately, this is another challenge that digital twins can help solve. By capturing the imagination of stakeholders from board level to the ‘shop floor’, digital twin benefits are easily visible to all – especially when they aim to solve existing day-to-day challenges.

Using digital twins powered by dynamic data sources means improvement teams don’t have to look to construction teams for input. They have all the same insights and information available to them directly. In turn, that creates opportunities to act faster and deliver improvements earlier to reduce cost impacts and maximise value.

But, for this dynamic data-based project approach to work, there are a number of data challenges that everyone involved in the BIM process must understand and overcome.

SIX KEY CONSIDERATIONS FOR ROBUST, VALUE-ADDING DIGITAL TWINS

From greater cost control and improved performance, to empowered teams across a project, the advantages of using BIM processes to create evolving digital twins can be huge.

But before creating BIM outputs and connecting them with dynamic data sources there are six key data considerations any organisation needs to make: acca certification

1) Data Integrity: Your insights are only as good as your data, both static and dynamic. So, you need to find a way to ensure that data maintains its integrity – and in a cost-effective manner.

2) Data Granularity: Similarly, not all data incorporated in design is relevant for operational use. A decision must be taken on what data sources are necessary to include at both design and operational stages.

3) Data Governance: Data interoperability is vital between the various technologies in a digital twin ecosystem. Differing open and proprietary standards and exchange formats will require careful consideration to ensure success.

4) Legacy Data: Greenfield projects can give the benefit of a blank canvas with license to take a digital-first mentality from the outset. However, when faced with existing assets and operations, a lack of data is often a problem. Taking a digital-first modernisation approach, with a focus on data development, will return dividends in these circumstances.

5) Human Factors: Silos within organisations can cause big problems. To succeed you need to take an empathy-driven approach that enables diverse teams to collaborate easily, and work together to deliver the best results at every stage of the project lifecycle.

6) Data Democratisation: The priority should be to make information from digital twins accessible to the end user, without them having to use complex IT technologies.

At Royal HaskoningDHV we have years of experience using BIM processes. Recently, these processes have been enhanced to deliver digital twins that help our clients optimise processes, lower costs, and design and deliver more efficient assets.

With our 140-year history in engineering, predictive simulation and data science capabilities, we’re perfectly positioned to help you overcome the challenges of dynamic data-driven BIM processes and take advantage of the huge opportunity digital twins bring.