What is a Digital Twin and what capabilities do you need to build one for your business?
Introduction
Data-driven decision making is no longer a nice-to-have for enterprise organisations, but a must-have in today's fast-paced, data-driven business world. By leveraging data insights and analytics, organisations can gain a competitive advantage, improve operational efficiency, and drive growth. In order to stay ahead in a constantly evolving market, it is crucial for enterprise organisations to become data-driven and adopt a data-first approach in their decision making processes.
For this reason enterprise organisations are increasingly considering the use of digital twin capabilities to enhance their decision making. Whilst enabling them to generate a 360 view of their financial & operational performance, their customers and the products they consume/interact with, as well as the overarching regulatory posture of the business.
What is a digital twin?
A digital twin is a virtual replica of a physical object or system, used to simulate, analyse and optimise real-world behaviour and performance. It consists of data, models and algorithms that mirror the attributes and functions of the actual object or system, providing insights into how it operates and how it can be improved.
Why do organisations build digital twins?
Organisations consider creating digital twins for several reasons, including:
Improved understanding of physical systems: Digital twins help organisations to better understand the behaviour of physical systems and predict how they will perform under different conditions.
Optimisation of operations: By analysing data from the digital twin, organisations can identify ways to optimise the performance of physical systems, reduce downtime, and improve efficiency.
Enhanced decision-making & simulations: Digital twins provide real-time data and simulations, allowing organisations to make informed decisions based on accurate and up-to-date information.This can be particularly useful for what-if scenario planning and impact analysis exercises.
Improved customer experience: Digital twins can be used to simulate customer interactions with physical systems, helping organisations to improve the customer experience and identify opportunities for innovation.
Predictive maintenance: By analysing data from the digital twin, organisations can predict when maintenance is required for a physical system, reducing the risk of unexpected failures and downtime.
Historically, digital twins have been linked to manufacturing and IoT based industries, but with the desire for every single organisation to be data driven it’s applicability has now widened to many other industries and domains.
Who is using digital twins and what business value have they created?
Examples of digital twins being used across the FTSE and Fortune 100 can be seen with growing adoption and complexity.
Rolls-Royce: Rolls-Royce created a digital twin of their engine control systems which enables them to simulate and test the performance of their engines in various conditions. This has led to a reduction in the time and cost of physical testing, increased reliability and efficiency of their engines, and improved customer satisfaction. CIO reports that Rolls Royce digital twin has helped it extend the time between maintenance for some engines by up to 50%, thereby enabling it to dramatically reduce its inventory of parts and spares. Furthermore, utilisation of digital twin capabilities has enabled the business to unlock carbon efficiencies for its customers that equates to 22 billion tons of carbon savings to date.
BP: BP invested in building a digital twin of their upstream operations which helps them to optimise production, reduce downtime and improve the efficiency of their operations. This has resulted in increased production, reduced costs, and improved safety. Further building on the carbon efficiencies and net zero benefits reported by Rolls Royce believe that digital twin technologies can help reduce CO2 leakage by around 500,000 tonnes every year if rolled out across their operational assets.
BAE Systems: BAE Systems has applied digital twin techniques to their military aircraft which enables them to test and validate design changes before physical prototypes are built. This has resulted in reduced development time and costs, improved safety, and increased operational efficiency. BAE has mooted that by leveraging digital twin capabilities it has reduced tasks that used to take months, to a matter of days.
Financial Services: Various FTSE/Fortune 100 financial services organisations are creating digital twins of their banking operations which allows them to simulate and test various scenarios and stress tests in a virtual environment. The intent is to improve their risk management posture, reduce costs, and increase the speed and accuracy of their decision making. In essence, firms are aiming to simplify and streamline regulatory reporting and operational risk planning efforts.
Digital twins invariably enable operational efficiencies, reduce risk and simplify business processes for enterprise organisations. However, in order to get started a set of foundational capabilities are required to ensure any digital twin solution is not built on quicksand.
What are the key capabilities required to build a digital twin?
First and foremost, patience is key! However, from a technology and procedural perspective, there are 9 foundational capabilities that organisations require. Broadly speaking these are:
Data acquisition: The ability to collect data from the physical system, sensors and other sources, and to integrate this data into the digital twin.
Modelling and simulation: The ability to create a mathematical model of the physical system and to simulate its behaviour using this model.
Analytics: The ability to analyse data from the digital twin to gain insights into system performance, identify patterns and predict future behaviour. More often than not this will include the adoption and implementation of graph database capabilities.
Visualisation: The ability to represent the digital twin and its behaviour in a visual format, allowing stakeholders to easily understand its performance and to identify opportunities for improvement.
Integration with other systems: The ability to integrate the digital twin with other systems, such as enterprise resource planning (ERP), customer relationship management (CRM) and Internet of Things (IoT) platforms. Or leverage architectures that enable you to surface, expose and democratise data using a data product oriented approach.
Security: The ability to ensure the security and privacy of data and models in the digital twin, and to prevent unauthorised access and manipulation.
Scalability: The ability to scale the digital twin to meet the growing demands of the physical system, and to accommodate new data sources and simulation requirements.
Continuous improvement: The ability to continuously update and refine the digital twin based on new data, insights and feedback from stakeholders.
Querying Engine: Deploying the use of a query engine that allows users to interact with the data and analytics generated by the digital twin in a consumer friendly way.
Typically, for any digital twin to scale in an optimal manner, organisations need to also ensure they have applied a strong, yet product oriented approach to data governance. Increasingly, we are seeing organisations adopt a federated approach to data governance and reinvigorating or further investing in data discovery solutions. Putting metadata at the heart of their governance approaches and leveraging data catalogues to provide a storage, indexing and search engine type capability for clearly defined, purposeful and owned data products. In short, making data as easily accessible as a quick google search in the enterprise.
How should organisations get started in building a digital twin?
Starting small and taking a use case driven approach is vital to ensuring that a digital twin serves a clear business purpose, whilst offsetting the risk of building a white elephant. Many organisations often fall into a “build it and they will come” approach which will only take the digital twin so far without any clear business demand from key personas or end users.
Typically, organisations should follow 5 key principles to support the early stage delivery of embarking on any such digital twin build. They are:
Define clear goals and objectives: Clearly define what you want to achieve with your digital twin, and how it will support your organisation's operations and goals.
Start with the right data: Ensure that the data used to build your digital twin is accurate, relevant, and up-to-date. Start with the most critical data sources and build from there.
Engage stakeholders: Ensure that key stakeholders, including business, technical and operational teams, are involved in the design and development of your digital twin. This will help to ensure its alignment with your organisation's goals and needs. Taking a use case driven approach is key. Otherwise you run the risk of building a white elephant and the perennial field of dreams.
Adopt an iterative approach: Building a digital twin is an iterative process. Start with a minimum viable product (MVP) and refine it over time based on feedback and results.
Invest in the right technology: Choose the right technology and tools to build your digital twin, and make sure they are scalable and flexible enough to meet the evolving needs of your organisation.
In Conclusion:
In conclusion, digital twins have the potential to revolutionise various industries and bring about numerous benefits. From improved efficiency and reduced costs, to enhanced safety and increased customer satisfaction, digital twins offer organisations a unique opportunity to gain a deeper understanding of their operations and make informed decisions. As technology continues to advance, it is expected that more and more organisations will adopt digital twins, leading to further innovation and growth. The future is certainly bright for digital twins, and we can expect to see exciting developments in this field in the years to come.
