Digital twins demystified: how to turn three digital threads into limitless business value
In business, we’d all like a crystal ball: given the events of the last two years, with the arrival of the pandemic and disruption of supply chains, the ability to manage uncertainty is more important than ever. This goes a long way to explaining why digital twins are in such demand.
A digital twin is a live, evolving digital replica of a physical asset (or process, system or service) that is supposed to be complete in all respects including the external environment where the physical entity exists. Digital twins bring new ways to look into the future; we can replicate and simulate scenarios for machines, plants, infrastructure, smart cities, even related processes. While the growth of digital twins is widespread, in the energy and utilities industry – where risk mitigation is particularly quick and critical – they are particularly advanced.
What’s driving digital twins?
With resilience firmly on the agenda for every company and industry, there’s a lot of pressure on supply chains and operational efficiency. What’s more, following the shift to digitalization, companies need to invest in automation tools and solutions, which will have a profound effect on the way people and processes interact. There is a critical need to contextualize data and build an effective knowledge base for secure, sustainable and optimized operations.
At the same time, modern day products have increasing levels of software components which demands new ways to design, build and maintain them. Moreover product designing is always done based on qualified assumptions within product lifecycle management (PLM), but always based on a specific context. When the context changes these qualified assumptions also needs to be optimized based on closed loop product feedback from the field. Through digital twins (in combination with PLM platform), companies can use an integrated Digital Twin meta model for analytics, AI/ML, changing the way products are built, services, managed and retired.
Three essential ‘digital threads’
Broadly digital threads can be categorized in terms of data structure, dynamics and frequency as input to a digital twin, with feedback loops:
- OT – unstructured data coming from the field, including IoT sensor data, in numeric, pictures, sound bytes or in video formats.
- IT – well-structured data from enterprise IT systems, including Product Lifecycle Management, Enterprise Resource Planning, Asset and Inventory management, or computer-aided design (CAD) etc.
- Semantic – the cognitive knowledge captured in knowledge graphs providing logical relationships through experience of people and systems (often the most under-estimated and least recognized of the three threads).
By combining and contextualizing the above three threads, one can achieve the Digital Twin Meta Model providing a 360˚ view of assets/products/processes that can be used to address key business problems, such as increasing the availability of assets, reducing cost of operating, and improving efficiency of the supply chain.
Top-down, business value-driven
The single most important thing is to apply the right approach to engage on a Digital Twin roadmap. A top-down approach with business problem as its starting point is the first critical step on the digital twin journey. Applying our 4M-6C consulting template we are in a good position to establish systematics, through business-driven approach to address specific KPIs.
A top-down approach with business problem as its starting point is the first critical step on the digital twin journey.
Creating and articulating the right problem is likely to require the involvement of different divisions within business. With varying perspectives and priorities, each stakeholder might view the problem slightly differently (in terms of technology, financing, operations, business strategy), which is why effective stakeholder engagement is so important.
Standardizing and industrializing
Enabled by digital twins, improving people, machines, materials and processes (which we sometimes call the ‘4Ms: man, machine, materials, methods’) creates the business improvement roadmap, evolving from decision support to predictive operations using a digital twin maturity model. This helps organizations transcend organizational siloes and coalesce around common objectives.
Atos’ own digital twin journey started in the wind turbine industry. Our digital twin platform has been the means to industrialize, verticalize and standardize (data models, processes, best practices) in order to provide robust end-user applications, with around 82-85% replicable within an industry.
Efficiency, availability, sustainability and customer promise
Energy and utilities remains a key focus; in the renewables sector alone, we support around 750 wind turbines from six different OEMs (with four different models per OEM) – a testament to these capabilities’ increased maturity. Digital twins help increase the speed and efficiency of complex field work, reducing Mean Time to Repair while enabling predictive inventory management, asset management, and plant operations and maintenance. Digital twins are also a critical enabler of decarbonization, enabling companies to more effectively generate carbon credits.
Ultimately, this is about meeting energy companies’ critical cost, resilience, sustainability and service objectives. We’ve seen, for instance, 15% reductions in operational costs for wind turbines. These equate to significant cost savings; and every 1% increase in asset availability translates to hundreds of thousands of units (and millions of euros’ worth) of power, underpinning energy service operators’ essential promise to end-customers.
Powerful evolving 360˚ view
For any organization investing in digital twin capabilities, the approach must be top-down and value-driven. Think of your digital twin as a puzzle: when you have all the digital threads, then the picture you create is the mechanism to analyze behaviors and simulate problems in the exact context of what can be remote environments.
Once you have that evolving 360˚ view, which can incorporate even the sounds that a machine makes, you can address all sorts of different use cases and KPIs, to achieve improvements and innovation that support growth and drive energy transition forward.