Why twins are so incredibly useful
Data in individual twins can be combined with data from other twins to gain new insights on new challenges and opportunities.
A new report from the UK’s Infrastructure Client Group shows that while organisations believe digital twins will be important for meeting future challenges and realising better outcomes for themselves and society, they are uncertain how to go about developing them. Ashleigh Monagle explains the three core principles organisations should follow to get started.
Known as ‘mirroring systems’, the first twins were developed by NASA the 1960s to predict faults in astronauts’ equipment – and on occasion to solve crises: a twin was used to find solutions during the ill-fated Apollo 13 mission, saving the crew from death. Fast forward to today and twins are increasingly digital. Digital twins are dynamically linked to the physical twins they represent by data. They are starting to be used to simulate anything from components and assets, through to infrastructure systems.
But the latest digital benchmarking report by the UK’s Infrastructure Client Group (ICG) – a snapshot of the digital maturity of the industry – shows that the majority of organisations are struggling to get started and realise significant value from digital twins. Focus is required on making twins purposeful, trustworthy and functional. Organisations report they are too siloed and lacking the collaboration skills to tackle shared challenges, or realise synergies from shared data. And they aren’t yet tapping industry resources and support. This finding emerged from our survey for the ICG, carried out using the Smart Infrastructure Index.
Why twins are so incredibly useful
Twins can be used for different purposes throughout the asset lifecycle and are delivering diverse benefits – better safety, efficiency, performance, reliability, profitability, and more.
They can be developed to address a specific stage in the lifecycle of an asset, building or infrastructure system, but it is possible for twins created at the outset to be evolved over the entire lifecycle. The fundamental purpose of twins is to enable better insight and better decisions, to achieve better outcomes. Two examples are:
- A dynamic model of an asset, with input of performance data from the physical twin via live data flows from sensors and providing feedback into the physical twin via real-time control.
- A static strategic planning model, with input of long-term condition data from the physical twin via corporate systems and providing feedback into the physical twin via the capital investment process.
The transformative potential of twins lies in their ability to be inter-connected. Data in individual twins can be combined with data from other twins to gain new insights on new challenges and opportunities. Organisations can join up twins they own; and twins owned by different organisations in different industries can be connected too. Within organisations, twins may serve to deliver and integrate new assets better, chase out operating inefficiencies, detect and fix faults, understand user behaviour or model carbon emissions, for example. Between organisations, twins may serve to understand and manage supply chain relationships, resource use, or shared risks that transcend organisational boundaries, such the physical impacts of climate change.
Developing digital twins can be a daunting prospect. But by following three guiding principles organisations can forge a clearer path to success.
1. Establish a purpose
Establish the business case by asking why you need a digital twin – what questions need answering, how will you turn insights into action, and what will be the benefits?
Focus on strategic goals and challenges your organisation and specific individuals are grappling with. Work out what data it is practical to collect – where from and how, and what is required to manage, store, use and present it.
2. Make a start (however small)
The ‘big bang’ approach of attempting to implement a comprehensive solution from day one is difficult to pull off. Instead, start small and learn as you go, working out and addressing key considerations – level of detail, quality, necessary resources and how best to realise the benefits.
Think of a twin as a work in progress for which the possibilities and capabilities will grow in line with your needs and technological advancements. Take an agile, iterative approach. Phase development, work with experts, learn by doing and keep up momentum by targeting smaller incremental wins. This will reduce the likelihood of burnout and improve stakeholder engagement along the way.
3. Collaborate and connect
Your purpose and benefits will likely be defined by multiple data sets and stakeholders, so collaboration is vital. Over half of respondents to the ICG digital benchmarking report agreed that corporate silos stifle collaboration and knowledge sharing, so it’s essential to break them down. Specialist external support can help initiate and guide the necessary collaborative behaviours. And there are external sources too: The Digital Twin Hub is an industry resource and proving ground for ideas and knowledge, connecting twin owners and operators, supporting innovation and standardising approaches to the development and operation of digital twins.
Simulation sees the unseen
Our design and build business MMB developed a digital twin of a £37M sludge treatment plant at Five Fords near Wrexham in Wales. Sludge treatment is part of the wastewater treatment process. It is a complex and highly controlled process, and must cope with variable conditions. We were responsible for designing, building and commissioning the plant for Dŵr Cymru Welsh Water. We and our client wanted to mitigate risk during the commissioning stage and get the plant up to full treatment capacity as quickly as possible.
The complex treatment process was simulated using industry-leading wastewater simulation software Biowin, integrated into our digital twin platform Moata. Moata can process billions of data points daily and integrate with multiple software packages and machine learning modules. It enabled the MMB team to operate the virtual plant as if in real time, making changes to the treatment regime to understand how to achieve the best possible performance.
Now the plant is up and running, the digital twin is still in use helping fine tune performance further, reduce energy use and cut chemical consumption. It has allowed the team to make decisions proactively, rather than reactively.
Moata hosts digital twins for the water and wastewater industry more widely, including supply, distribution and treatment, and catchment models linked to rainfall data.
We’re only at the start of the digital twin journey, increasing interest and uptake of the technology, but we’re seeing real early wins, and the benefits are growing over time.
Read our executive summary of the ICG’s digital benchmarking report ‘National recovery, digitally driven’
Read more about the guiding principles for digital twins