Digital Twin or White Elephant?

Advances in digital technologies and data analytics offer fantastic potential for organisations to get better value from their infrastructure and assets. ‘Digital Twins’ are touted as one of the key technological developments that can drive a step change in decision-making. But how do you make sure that investing in a Digital Twin will give you your expected rewards? We are helping our clients navigate the ‘wild west’ of Digital Twins and have set out six factors for deploying them to drive value in your business.

All around the world, organisations operating and managing critical infrastructure and assets are facing deep uncertainty and new, emerging risks. From mainstream asset-intensive sectors, such as transport, power, and utilities, to others including defence, retail and real estate, there are multiple challenges (and opportunities) that are increasing the trade-offs and complexity inherent in making strategic and tactical decisions. Climate change, cyber security, evolving standards, shifting demands, evolving customer focus, ageing assets and financial pressures are just some of the things making it difficult to deliver well-judged, timely interventions.

At KPMG, we are helping our clients to cut through this complexity by helping them adopt, deploy, and use the concept of digital twins as part of their suite of decision support tools.

Digital twins can drive enhanced strategy and planning, improve performance management, and provide assurance in risky and dangerous environments.

But digital twins are often shrouded in complexity. The combination of technical terminology, rapid change, and sector-specific jargon means that it can be extremely hard to understand what a digital twin is.

The UK’s National Digital Twin programme (NDTp) sets out the following definition:

A digital twin is a digital representation of a physical asset, process or system. It is distinguished from any other digital model by its dynamic connection to the physical twin. A digital twin unlocks value by supporting improved decision-making.

In most cases, digital twins will be made up of five foundational capabilities:

1. Sensors – Sensors distributed throughout a process or to monitor assets create signals that enable a digital twin to capture operational and environmental data pertaining to the real world.
   
   
2. Data – Real world operational and environmental data from the sensors is then aggregated and combined with other relevant data.
   
   
3.  Integration – Integration technologies communicate data to the digital world (e.g., edge, communication interfaces, and security), forging a link between the physical world and the digital world.
   
   
4. Models & analytics – Data modelled and analysed via algorithmic simulations and visualisation routines, which produces actionable insights.
   
   
5. Actuators – Where an action is warranted in the real world, actuators trigger, alter or inform the physical process. Actuators can involve automated or human processes.

While these foundational capabilities are important to understand, building a digital twin involves more than just procuring a complex array of different technologies.

As clients to embark on their own digital twin journey, significant obstacles stand in the way of success. Confronting those challenges will undoubtedly present hard questions - yet a brighter future awaits those who do pose these questions and seek to provide answers.

We have identified six challenges that we often see, with a view on how clients can overcome these obstacles to build scalable, extensible, connectable, and interoperable digital twins.

1. Setting a clear direction
   
   A digital twin can affect many aspects of a decision-making systems, impacting different people and change programmes over time. This might seem obvious, yet all too often the problem context for a digital twin is ill-defined, and the activities used to set out the strategic purpose of a digital twin frequently lack protocol or rigour.
   
   As such, it is vital to employ inclusive methodologies (particularly those from systems or design thinking circles) that enable you to empathise with those affected and that help you to build up a holistic picture of the problem that you are setting out to solve.
   
   
2. What information do you require?
   
   When you are armed with a clear, representative view on why you are developing a digital twin, it is vital to determine the necessary level of information that you require. In short, this means undertaking diagnostics to understand what information you require to decide or intervene in the real world. Failure to do so could result in your digital twin initiative missing the mark in terms of the insights it delivers to decision-makers.
   
   In many cases, information requirements may have already been defined. So, it is extremely important to consider how your digital twin initiative aligns with your existing enterprise architecture and process models. Where information requirements are ill-defined, you will need to take the time to figure out what you need, and how you intend to get it.
   
   
3. What quality of data do you need?
   
   Good quality data is essential for any digital twin. Yet the level of data quality that is deemed ‘good’ can vary significantly. For example, if the real-world asset, process, or system is extremely complex, it may be difficult and time consuming to obtain trustworthy, detailed data on every element. Similarly, the importance of the decision that you are looking to take, and judgements on how often you want to make decisions determine what level of data quality is acceptable.
   
   Digital twin developers should engage closely with decision-makers to ensure that there is consensus on what good quality data means. In practicality, this means considering the accuracy, level of abstraction and rate of data transfer that will be built-in to the digital twin. Failure to pinpoint precisely what quality of data you require could mean that your digital twin is useless, or prohibitively complex to develop.
   
   
4. Baking-in security mindedness
   
   Digital twins are characterised by their close interconnection with the real world. This close interconnection is what makes digital twins valuable. But this characteristic also increases the risk of security threats and can expose vulnerabilities in real-world systems. Mismanagement of these risks could have extremely damaging, and cascading effects.
   
   As a result, all business leaders, managers, and practitioners should take a proactive approach to embedding ‘security-mindedness’ in their digital twin initiative. This will help to deter and disrupt hostile, malicious, fraudulent, and criminal behaviours, and protect you against the loss of valuable commercial information, personal data, and intellectual property.
   
   
5. Getting the right flow of data and components
   
   One of the most exciting aspects of building a digital twin involves enabling it to talk to, interact and learn from other digital twins. To achieve this state, digital twins must be federated or connected. In plain English, this means getting the right flow of data and components that you require to tackle the problem your digital twin is looking to address. In practicality, this can be extremely difficult to achieve.
   
   In the UK, the CDBB is working hard to develop an industry standard for interoperable digital twins – known as the Information Management Framework. Additionally, firms like Iotics are already implementing technical solutions that enhance clients’ ability to federate digital twins. Strong digital twin initiatives will look at both existing solutions on the market and develop detailed plans around how their digital twin could be refactored to the IMF approach in the future.
   
   
6. Legal foundations
   
   Finally, you must have a clear legal foundation for the development of your digital twin. Many potential parties and individuals can be involved in a digital twin initiative, with many different services, decision hierarchies and data flows to keep track of. Additionally, the strategic purpose of a digital twin can be highly dynamic, meaning early legal frameworks may become rapidly outdated as your twin evolves in scope. There is also a whole suite of issues associated with access, liability and intellectual property that should be acknowledged.
   
   In short, the process of legally protecting your digital twin can quickly turn into a highly costly contractual “spider web”. Adopting a dynamic and responsive approach to legal protection early on is therefore imperative. Understanding the legal implications of variations in digital twin design or use will also be key.

Digital twins can help us to make well-informed, timely decisions- particularly in highly uncertain and risky environments. Yet, digital twins are complex and can be very difficult to design, develop and operate.

A robust approach to building a digital twin involves setting direction, defining information requirements, and ensuring that you have good quality data that will enable you to support and manage the digital twin appropriately.

Innovators will need to consider security threats, how the digital twin will talk to and learn from other twins, and where legal risks may present themselves.

This might seem like a daunting set of issues to work through, but by taking these six points into consideration, your digital twin might have a chance of survival!