A digital twin is a virtual replica of a physical object, process, system, or person. It uses real-time data relayed from the physical asset to mimic its behaviour in different contexts. Digital twins let businesses in all industries simulate authentic situations virtually, allowing them to make more informed business decisions.
Historically, every technological advancement has relied on physical prototypes, iterations, conceptual drawings, and a bucket load of trial and error. Consider what would have happened if the Wright brothers hadn’t created dozens of prototypes of their famous flyer back in 1903. We likely wouldn’t be celebrating Orville and Wilbur like we do today.
But in the digital age, we don’t need to rely on trial and error to test the effectiveness of a physical asset. Digital twins allow you to create virtual prototypes that you can use to evaluate objects and make accurate forecasts and better decisions.
And the best part is that you can do all of this without the costs, risks, and disruptions of physical testing. You might not find it surprising that almost 75 per cent of companies in advanced industries have already adopted this technology to optimise their operations.
Digital twins can be hugely beneficial for modern businesses. But there’s a lot to consider if you want to implement them correctly. Let’s discuss how to do just that and closely examine the tech behind the concept.
What is digital twin technology?
A digital twin is a virtual representation of a real-world asset, such as a physical product, system, or service.
This virtual twin appears and works identically to its real-life counterpart, but it is entirely digital. By placing this virtual representation in simulated situations, businesses can gain deep insights into how real-world assets function and perform.
Digital twins can mirror lots of different real-world items. For instance:
- A business might create a digital twin for a piece of machinery in a factory, allowing it to predict maintenance requirements.
- Utility companies like Shell create digital twins of their offshore platforms to identify potential issues before they arise.
- Civil engineers could even use digital twins to replicate an entire city, letting them determine how to structure transport links or building layouts.
Digital twins link to real-world data sources. This means that they can update in real-time to mirror real-life assets. For example, a digital twin linked to sensors on a plane engine could monitor temperature and pressure, letting engineers discover and fix issues before the machinery fails.
To sum up, digital twins enable organisations to simulate, visualise, and analyse real-world objects to make better decisions, optimise operations, and prepare for the future.
How do digital twins work?
The digital twin is powered by the Internet of Things (IoT) – a network of physical objects that can connect and send data to other devices. Many digital twins are also bolstered by artificial intelligence (AI) and machine learning (ML), which can help with predictive analytics based on available data.
That said, to fully understand how digital twins transform information into strategy, we need to break it down. We can generally split the technology into three parts:
- Hardware
- Middleware
- Software
Hardware and IoT devices
Hardware components are the real-world, physical elements of the digital twin. Businesses will fit IoT devices like sensors and actuators to the object of study to support data collection and data integration. This real-world information can then be used to build the virtual replica.
For instance, a manufacturer could fit a car engine with temperature, pressure, and vibration sensors to gather data about the engine’s performance. This relays data to the middleware and ensures the manufacturer has all the information they need to build a replica.
Middleware
Middleware components bridge the gap between hardware and software. They allow data flow and integration, ensuring the data from the sensors can be collected and processed for software analysis and simulations.
In the case of a car engine, the manufacturer will typically use an IoT gateway to transmit engine data to their software of choice.
For instance, Waylay Digital Twin enables you to integrate your physical hardware with the Salesforce platform. This gives you an overview of all your crucial IoT assets within Salesforce, letting you predict and address issues, optimise asset performance and provide proactive service delivery.
Software
The software components receive real-world data through the middleware. They then employ this information to build visual representations organisations can use to analyse the object and make strategic decisions. This process is often combined with machine learning to make informed predictions.
For example, with the data provided by their IoT gateway, the car manufacturer can now build a digital twin to monitor their engine’s performance in real-time. If the engine isn’t functioning as intended, the digital twin will reflect this, allowing engineers to make adjustments proactively.
Digital twins vs simulations: How do they differ?
A simulation is another decision-making tool that simulates real-world business scenarios in built environments. While this method shares some similarities with digital twins, both approaches have several differences.
| Feature | Digital Twin | Simulation |
| Data analysis | Digital twins use real-time data to update the digital model instantly. | Simulations are based on predetermined data sets and variables. |
| Models | Digital twin models are always complex and multi-layered to mimic real-world counterparts. | Simulations can either be complex or simple, depending on use cases. |
| Timeframe | Digital twins update constantly and mirror the life cycle of their real-world counterparts. | Simulations typically only provide information for a specific scenario or point in time. |
| Customisation | Experts can interact with digital twins in real time to experiment with different scenarios. | Once experts feed the data and variables into the simulation, they have limited opportunities to alter the input. |
4 types of digital twins
There are four core levels of digital twins. Let’s explore each.
1. Component twins
The component twin is the most basic type of digital twin technology. It consists of digital models made up of various components and parts, like IoT sensors, switches, valves, and motors. This virtual twin offers detailed information about these individual parts, allowing businesses to monitor component performance to optimise efficiency.
2. Asset twins
Also known as a ‘product digital twin,’ this variation consists of several component twins that combine to form a more complex asset, such as a car engine. This type of digital twin provides real-time data revealing how effectively the components interact and perform as part of a larger solution.
3. System twins
System twins work on a broader scale to provide an overview of an entire system, like a wind farm. This allows businesses to evaluate different layouts and configurations to determine which are most efficient. They can then use this to make informed decisions to enhance overall productivity.
4. Process twins
Process twins are the highest level of digital twins. This solution explores how different physical systems collaborate. It provides a broad view into a system or process, such as manufacturing processes or supply chains, allowing organisations to see how every system interacts with the other.
What are the benefits of digital twins?
Digital twins are big draws for businesses. Let’s run through a handful of the benefits of digital representations to show how they can help.
- Improved efficiency and productivity: Operating more efficiently is a broad goal for many organisations, but with data, it can be easier to know where to start. Digital twins offer the real-time insights businesses need to improve performance throughout the value chain.
- Better decision-making: Digital twins provide valuable insights businesses can use to make informed decisions organisation-wide. By offering real-time data, they help companies to adapt, remain agile and make strategic decisions.
- Product design and development: Using a digital twin can help organisations research and design better products. The digital twin software can answer all ‘what-ifs’ and make predictions about likely outcomes in different scenarios, helping companies iterate products before they begin manufacturing.
- Cost savings: Simulating and visualising isn’t cheap, but it’s much more cost-effective than building various prototypes and dealing with problems after they occur.
- Remote tracking: As digital twins can be managed virtually, there’s no need to be on-site to detect a problem. Businesses can control everything remotely, keeping costs down and reducing safety risks.
Use cases of digital twins
Digital twins have use cases in dozens of industries. Let’s explore some potential applications in unique sectors.
- Manufacturing: Digital twins can monitor everything from individual pieces of machinery to entire assembly lines and factories, improving efficiency and reducing downtime.
- Urban planning: Civil engineers can employ digital twins to support infrastructure planning by optimising transport links and building designs. They can also integrate these twins with augmented reality systems to create an explorable virtual city within a built environment.
- Healthcare industry: Sensor data can be used for real-time health monitoring. Integration with AI will allow medical professionals to identify concerns retroactively rather than waiting for a problem to occur.
- Retail: Retailers could build digital twins of their ideal customer personas to evaluate and improve the customer experience.
- Energy sector: Digital twins of renewable energy systems can optimise energy production and predict failures, helping companies manage resources effectively.
- Supply chain management: By employing process digital twins of large-scale supply chains, businesses can experiment with different logistical approaches to reduce bottlenecks and massively increase efficiency.
- Construction industry: A digital twin in construction allows experts to track infrastructure progress and identify problems before they arise.
We’re only scratching the surface here. The truth is that digital twins can benefit almost every industry worldwide. Let’s examine a case study to reveal how organisations use this technology to their advantage.
Digital Twin Victoria case study
Digital Twin Victoria is the Victorian Government’s $37.4 million initiative designed to prepare Victoria for the future. Through the state’s digital twin, the government intends to optimise every aspect of Victoria from top to bottom. Key aspects of the initiative include:
- Investing state-wide in datasets for 3D models of landscapes, cities, and buildings.
- Streamlining lengthy assessment processes by automatically checking applications against building and planning codes via the digital twin.
- Creating a Digital Twin Victoria (DTV) platform to display real-time data that helps businesses in all sectors.
- Responding proactively to disasters through predictive satellite imagery and 3D visualisations.
The creation of the DTV is particularly exciting. This browser-based tool will be available to all Victorians and can help even those with no experience with spatial data.
The platform will provide a wealth of data, from 3D buildings of Melbourne to the live habitat of Phillip Island penguins. It can be applied to several scenarios:
- Develop smart cities with innovative transport systems, vegetation, and buildings.
- Support disaster management and solving energy problems.
- Help preserve natural resources and support climate change efforts.
- Help planners and engineers investigate feasibility and solve issues.
- Support automatic regulatory decision-making.
The Digital Twin Victoria initiative is the perfect example of the depth and breadth of digital twins. With an innovative approach, anything is possible.
Digital Twins: Challenges and Considerations
Digital twins offer dozens of benefits, but this doesn’t mean they’re without their challenges. Here are a few you should consider.
Data quality and availability
Digital twins rely on quality data. The information must be precise, timely, accessible, and readily available. As digital twins provide so much data, it can be challenging to sort through this information and discover what’s useful and what isn’t.
As such, it’s vital to implement robust data governance frameworks for cleaning, storing, and analysing structured and unstructured data. This can be a significant investment — you may need to invest in staff or artificial intelligence to help with this process.
Technical complexity and costs
Digital twins have a relatively high barrier to entry. They can be technically complex and expensive to set up. While businesses that build virtual twins can significantly reduce long-term costs, the upfront investment is a hard roadblock to bypass.
That said, as the technology becomes widely used, the entry cost should decrease, which will likely be a short-term challenge.
Interoperability
Your digital twin doesn’t operate in a vacuum. It needs to play nicely with all of your other systems, software, and computers. While these integrations will become easier in the future, the road to seamless interoperability isn’t straightforward.
In the meantime, it’s essential to choose digital twinning solutions that work well together. For instance, Waylay Digital Twin integrates with Salesforce, allowing you to visualise and explore your IoT data directly in the platform.
Ethical implications
Lastly, there are two primary ethical concerns you should consider:
- Data privacy and security: If your digital twin generates sensitive information about your business, products, or customers, you’ll need to implement data protection strategies to identify, classify, store, and use this information in line with legislation. You may be required to implement access controls and a thorough code of conduct to secure sensitive data.
- Bias: Bias can stem from faulty sensor readings, incomplete datasets, or even artificial intelligence models. This can create inefficiencies and lead to false outcomes. As such, it’s important to collect a diverse range of data to find outliers. You should also continually evaluate your model to ensure it is reliable.
The future of digital twin technology
The digital twin market was valued at a respectable 12.91 billion in 2023. By 2032, it’s forecasted to reach an astonishing 259.32 billion.
The application of digital twins is currently widespread in advanced industrial sectors. However, the new opportunities of Industry 4.0, 5G connectivity, cloud computing, and artificial intelligence (AI) mean businesses in various industries will soon look to leverage the wide range of use cases that digital twin models offer.
Unlock the full potential of digital twins with Salesforce:
- Predict outcomes using the Salesforce Einstein 1 Platform.
- Seamlessly integrate through Salesforce MuleSoft.
- Visualise insights with Salesforce Tableau.
- Automate field service through Salesforce Field Service Lightning.
Organisations in industries as diverse as healthcare, transportation, and automotive will begin to employ digital twins for tasks like compliance monitoring, sustainability, maintenance, employee tracking, and risk analysis. The democratisation of digital twin technologies is fast approaching.
The key to implementing a digital twin is to prepare and plan diligently. Know the barriers, understand the challenges, and establish best practices before you begin.
Get it right, and you’ll have the opportunity to leverage the benefits this groundbreaking technology offers.
