What Is a Digital Twin?

A digital twin is a dynamic replica of a real-world object or environment that continuously updates using multimodal data streams based on real-world inputs. For example, imagine a virtual re-creation of a passenger car displayed on the car’s dashboard touchscreen. Sensors can continuously update the representation with data about the vehicle’s tire pressure, fuel level, and acceleration. This type of digital twin provides better observability to the driver and supports AI processes such as autonomous driving.

Types of Digital Twins

To be considered a digital twin, a system needs to include both:

• A virtual representation of a real-world object or environment
• Data inputs that reflect real or simulated changes to that object or environment

This type of system can take many different forms. For example, Google Earth is a digital twin because it is a virtual representation of Earth that is updated with real-world inputs.

Similarly, a fitness app that shows a generic, digital model of a human body populated with real-world data, such as the user’s heart rate, could also be considered a basic type of digital twin. Even though the digital model is not personalized to the user, it shows a virtual representation of a real-world object and updates that representation with real-world data.

Virtual Twin vs. Digital Twin

Digital twins and virtual twins are distinct concepts, although they both refer to a digital re-creation of a real-world object. Digital twins represent existing real-world objects informed by real-world data inputs.

Virtual twins, on the other hand, may precede the creation of the object they represent, and they do not rely on real-world data inputs. Virtual twins are mostly used to prototype new products and simulate their performance in various conditions before the object goes into production.

Benefits of Digital Twin Technology

Digital twins open the door to new possibilities for observation, simulation, and automation across virtually any industry. Key benefits include:

• Deeper visibility: Digital twins offer a new way of seeing objects or environments, informed by real-world data. They also serve as a foundation for experimentation by allowing digital twin operators to efficiently simulate the effects of changes or different conditions.
• Cost optimizations: With greater visibility, digital twin operators can stay informed of potential issues or errors in a digitally twinned asset. Digital twin operators can also map these issues against historical data to drive predictive maintenance, extending the lifespans of assets and helping minimize downtime.
• AI and automation: While digital twins help inform human decision-making, they can also be used to inform automated processes. For example, autonomous mobile robots (AMRs) used in industrial settings can digitally map their environments as they navigate using real-time data collected from cameras and sensors. This allows for intelligent route planning and hazard avoidance.

Challenges of Deploying Digital Twin Technology

To be effective, digital twins need to be able to pull sensor data from edge computing environments and aggregate the data into centralized platforms. Organizations that rely on legacy technology may encounter challenges with data silos, lack of reliable connectivity, and the complexity of managing data from several disparate devices. Depending on the type of deployment, organizations may also need to ensure data privacy and security to comply with regulations for sensitive workloads.

Fortunately, digital twin orchestration platforms can help organizations onboard sensors and other equipment, manage data sources, and support common use cases for spatial awareness or asset tracking. Modern edge-computing hardware can also feature built-in security features to support data privacy and compliance while providing the compute power needed to support complex digital twin workloads, including AI.

How Do Digital Twins Work?

In most digital twin deployments, sensors and other edge devices provide data to a virtual model running as software on one or more edge servers. These data inputs update regularly, usually in near-real time, so the digital twin is a close virtual replica of the real world.

Digital twins can exist for a specific appliance or piece of machinery or encompass an entire facility. In the healthcare industry, practitioners can use biometrics data to enable a digital twin of a human body.

Digital Twin Use Cases

Digital twins are a still-maturing technology with several applications in existing industries and innumerable possibilities that are yet to be imagined. Here are just a few examples.

Manufacturing

Digital twins of factory appliances help operators understand asset conditions and plan predictive maintenance schedules. Manufacturers using digital twins of floor plans can simulate the impact of production line adjustments and optimize for the best workflows. Digital twins can also help plant owners automate awareness of safety zones to detect hazards or unauthorized personnel.

Automotive

Car manufacturers are already using virtual twins to prototype and simulate new vehicle models. In the near future, digital twins will open up new possibilities for software-defined vehicles and self-driving cars. Digital twins can help human and AI drivers to better understand the condition of their vehicles.

Energy

Digital twins can be applied to energy grids and microgrid technology to help utility operators manage and optimize energy distribution. The flexibility of digital twins empowers utility operators with new tools to proactively respond to maintenance needs, simulate emergency scenarios, and plan for the integration of renewable energy sources in a cost-efficient manner.

Healthcare

Healthcare providers can use digital twins to enhance patient monitoring tools. By combining the data from multiple patient devices into a digital twin, providers can gain a more comprehensive view of patient health. Digital twins of healthcare facilities can also help administrators monitor environments for occupancy rates and safety concerns or even track air quality to help manage and prevent the spread of airborne pathogens.

Telecom

Digital twins can help telecom providers achieve a holistic view of network infrastructure, better understand asset health, and enable predictive maintenance to help minimize downtime. Telecom providers can use digital twins to simulate fluctuating demands on networks for stress testing or contingency planning without committing to costly real-world changes.

The Future of Digital Twin Technology

Digital twins are expected to become increasingly prevalent across industries. Even though 47 percent of IT decision-makers have never heard of digital twins, 95 percent of IoT platforms will contain some form of digital twin capability by 2029.¹

As organizations deploy more devices and compute resources at the edge, digital twins will likely serve as a focal point for consolidating and acting on these new data sources. Security and privacy will be essential to unlocking the value of digital twins to help ensure that no edge device becomes an entry point for malware. Digital twins will also help empower investments in AI and automation by providing a platform for applying intelligence to spatial and asset awareness.

While the horizon of digital twins offers many possibilities, the beginning of a digital twin deployment can be very simple. Organizations can start with a basic virtual replica of a real-world asset and connect a single data input to create a rudimentary digital twin. Over time, organizations can layer in more inputs and varied data sources to create a rich digital twin with the potential for multiplicative value.