Digital twins: how the enterprise metaverse can help your business grow

Imagine having a virtual replica of yourself, which you could use to simulate reactions or outcomes of certain behaviours, before testing them out in real life. Useful, right? Now project that to a business level. What if you could have a digital copy of a product your company is working on to test its behaviour in different scenarios and understand how it could be optimised?

Well, it is possible to access that enterprise metaverse thanks to digital twin technology. Let’s deconstruct this concept and see how it can help businesses achieve a strategic advantage.

What is a digital twin?

A digital twin is a virtual copy of a physical object, system, or process, used to create simulations that help predict behaviour and make better-informed decisions about performance and maintenance.

Here’s a simplified representation of how it works:

That is to say that this technology relies mainly on:

• Real-time data collection
  Relevant data regarding the physical object’s functionality, performance, and surroundings are continuously gathered by smart sensors and Internet of Things (IoT) devices. That data is what feeds the digital twin, to make it as accurate as possible when mirroring its physical counterpart.
  
  
• Cloud Computing
  Before the collected data can be analysed, it needs to be stored somewhere. That is where Cloud Computing comes in, by providing the necessary data storage technology to keep large volumes of data safe and accessible.
  
  
• Artificial Intelligence (AI) and Machine Learning (ML)
  Advanced AI and ML algorithms allow the virtual product to learn from the data that’s being collected and processed in real-time, which enables predictive analytics and maintenance, patterns’ identification, process automation and optimisation, and faster decision-making with minimal human intervention.

What is the difference between a simulation and a digital twin?

Both are virtual models that intend to reproduce reality, but the difference between them is fundamentally a matter of scale.

A simulation is a much more simplified model, commonly used to study one specific scenario, because it does not feed on real-time data.

A digital twin, on the other hand, is a much more complex virtual environment, which can continuously learn from data and create a vast number of simulations to study different scenarios.

Examples of digital twins across industries

The digital twin technology is being widely adopted across several sectors, especially those focused on large-scale products:

• Construction
  Accurate digital representations of buildings and infrastructure projects help architects and construction companies visualise the result, identify potential challenges, and optimise resources’ management even before construction begins.
  
  
• Transport
  Mechanically complex projects, like aircraft and automobile manufacturing, are leveraging digital twins to build each component as efficiently as possible and to maximise performance.
  
  
• Energy
  Virtual replicas of wind farms or solar power plants contribute to more efficient designs, more reliable components, and reduced maintenance costs.
  
  
• Healthcare
  Digital twins of patients and their organs help medical experts make faster and more effective diagnoses, develop personalised treatment plans, and predict disease progression. On the technical side, they also support medical staff training and simulation, thus driving innovation.
  
  
• Smart cities
  Urban planners are using digital twins to model city infrastructures like those already mentioned (buildings, bridges, transportation systems, public spaces, traffic flow, energy consumption, etc.).
  
  
• Banking
  A digital replica of banks’ processes and systems can help simulate the introduction of new customer support services, as well as monitor the performance of physical equipment (like ATMs), which ultimately helps improve services, products, and customer experience.

Did you know…

… Google Earth and Google Maps are two of the most notorious examples of digital twins. They both replicate the Earth’s surface in near-real time.

Imagine you’re going from point A to point B and open Google Maps to check out the best route. It then processes real-time traffic data gathered from IoT devices (like cars with GPS capabilities), mapping out the fastest route at that particular time.

Types of digital twins

There are different types of digital twins, depending on the scale of the product they are trying to replicate and the area of application. They can co-exist within the same system:

• Component twin
  The smallest example of a digital twin is a representation of a single (but vital) piece of an entire system or product.
  Example: a piston or valve in a car’s engine.
  
  
• Asset twin
  An asset is what we call a group of two or more components working together. This type of digital twin enables the analysis of the interaction between these components.
  Example: a car’s engine.
  
  
• System twin
  It shows how different assets function together to form a system, offering better performance insights.
  Example: the entire mechanical structure of a car, combining engine, suspension, etc.
  
  
• Process twin
  At a macro level, this type of digital twin shows how different systems (the studied object and its environment) interact and work together to form an entire production facility or entity.
  Example: the process of how a car is manufactured within the factory (or even the car itself).

Why do companies benefit from digital twins?

Digital twin technology can help businesses increase productivity, innovation, and competitiveness. Here’s how:

• Greater efficiency and performance
  Digital twins allow companies to do extensive research about a product’s design and performance before production even starts. This allows for a more efficient development process and a better-quality end product.
  
  
• Reduced time-to-market
  An optimised product design and a more efficient process means minimal waste of resources and time, so that product will reach consumers much faster than if it was developed without the help of a digital twin.
  
  
• Better predictive capabilities
  By analysing both historical and current data, Machine Learning algorithms can estimate maintenance needs, predict certain behaviours or issues before they even happen, allowing companies to take preventive measures and minimise downtime.
  
  
• Remote and continuous monitoring
  A digital twin allows you to remotely monitor your product or facility, at all times, which reduces the need of resource allocation for that specific task.
  
  
• Environmental sustainability
  A digital twin is particularly helpful in reducing environmental waste, since studying the product’s design before production itself can help organisations optimise the use of materials.
  
  
• Cost optimisation
  Everything we mentioned before leads to this: by improving efficiency and performance, preventing issues and downtime, and enhancing resource allocation, businesses can substantially reduce operational expenses.

How to get started with digital twin technology

Before anything else, certain conditions must be met, namely:

• Having a high-quality data infrastructure.
• Ensuring compliance with data privacy regulations.
• Having a specialised and multidisciplinary team to maintain that infrastructure and all that a digital twin implies.

Then, developing a digital twin for your business usually involves 6 steps:

1. Deciding on the type of twin that best responds to your needs.
2. Defining a roadmap.
3. Creating a blueprint.
4. Designing and implementing that digital twin.
5. Monitoring and improving.
6. Scaling or expanding the digital twin’s capabilities.

Ideally, an organisation would start with one digital twin and then work its way up to several interconnected digital twins – depending on what makes sense for each company/industry.

Challenges and future developments

While digital twins may provide a handful of benefits, they also face quite a few challenges that most of the AI and IoT technologies do, like:

• Data integrity: the accuracy and quality of the data used by Machine Learning algorithms have to be ensured at all times.
• Data security and privacy: companies need to comply with privacy laws and make sure that the digital twin technology is transparent and ethical.
• Infrastructure capabilities: the infrastructure supporting the digital twin technology must be robust enough to support scalability (more data, more storage space, more processing power).
• Expertise: for the time being, it is not easy to find qualified professionals to implement and manage Machine Learning and Big Data technologies. 
  
  

Despite these challenges, the future still looks promising for digitals twins. As AI, ML and Cloud Computing technologies mature and evolve, creating digital twins to support businesses will become a more accessible and cost-effective solution. Performance-wise, the sky is the limit since digital twins are constantly learning from data and developing new capabilities.

According to the renowned ScienceDirect, “more and more industries are actively using digital twin solutions for asset and product lifecycle management”, so “it is predicted that the technology will expand to more use cases, applications, and industries” in upcoming years.