Digital twin glossary

As businesses aim to boost efficiency, innovate services and simplify processes, digital twins are essential in digital transformation.

A digital twin is an exact digital copy of the physical world, using real-time data to simulate, analyse, monitor and optimise performance. However, understanding digital twins goes beyond a single concept. It involves various technology fields, each playing a part in creating a digital twin. These fields include simulation, Internet of Things (IoT) machine learning, data analytics, cloud computing and more.

This glossary clarifies these concepts, making the complex world of digital twins easier to understand.

Digital twin key terms and concepts

3D modelling: Creating a digital 3D version of a structure or object. For digital twins, 3D modelling shapes the digital copy of the real-world item.

Artificial Intelligence (AI): The simulation of human intelligence processes by machines. Digital twins use AI to analyse data, predict outcomes and automate decisions to boost efficiency.

Asset management: Managing the development, operation, maintenance and sale of assets effectively. Digital twins improve asset management by providing real-time insights into how assets are performing and what they need, allowing for better tracking and monitoring.

Augmented Reality (AR): A technology that overlays digital elements onto the real world, enhancing physical objects with computer-generated information. AR makes digital twins more interactive and understandable by adding digital information to the physical environment.

Automation: Using technology to perform tasks without human help. Digital twins support automation by providing the necessary data and insights to automate processes.

Big data: Large and complex data sets. Big data feeds into digital twins to create accurate representations, predictions and simulations.

Cloud computing: Delivering computing services over the internet. Cloud computing offers the resources needed to store and analyse the large amounts of data generated by digital twins.

Connectivity: The capability of devices and systems to link and share information. Connectivity is crucial for digital twins, as real-time data collection and analysis rely on smooth data exchange between systems.

Cyber-physical systems: Systems that integrate digital computers, networks and physical processes. Digital twins are a cyber-physical system combining physical elements with digital replicas for better monitoring and analysis.

Data analytics: The process of examining data to uncover patterns and correlations. Digital twins leverage data analytics to transform collected data into actionable insights.

Digital thread: The interconnected data flow that supports a product, system or asset throughout its lifecycle. A digital twin embodies this digital thread, linking different data sources and systems for integrated monitoring and decision-making.

Digital transformation: Integrating digital technology into all areas of a business. Digital twins are key to digital transformation, helping companies use real-time data and predictive analytics to improve operations.

Digital twin: A precise digital representation of the physical world that uses dynamic data to simulate, analyse, monitor and optimise performance

Edge computing: Moving data processing and storage closer to where it is needed. For digital twins, edge computing speeds up data processing and reduces delays in operations.

Integration: Combining components to ensure they work together. In digital twins, integration merges the physical and virtual realms into a unified system.

Interoperability: The ability of different systems to connect and communicate. This is critical in digital twins, as they must interact smoothly with various devices, systems and platforms.

Internet of Things (IoT): A network of connected devices that exchange data and communicate with each other. Digital twins often rely on IoT, using data from these devices to inform their models.

Lifecycle management: Managing a product or asset from inception to disposal. Digital twins optimise this process by offering continuous monitoring and advanced analysis, guiding decision-making throughout the lifecycle.

Machine learning: A type of artificial intelligence that allows systems to learn from experience. In digital twins, machine learning predicts future outcomes, improves operations and enhances decision-making using past data and trends.

Operational efficiency: The ability to deliver products or services in the most cost-effective way. Digital twins help businesses find areas for improvement and make data-driven decisions to streamline operations.

Performance optimisation: The process of making systems more efficient and effective. In a digital twin, performance optimisation ensures that the twin and its physical counterpart operate efficiently using real-time data and predictive analysis.

Predictive analytics: Using data, statistical algorithms and machine learning to forecast future outcomes. This is essential for digital twins, helping them anticipate system failures, maintenance needs or other future events based on past data.

Predictive maintenance: This approach uses data analysis to predict when equipment might fail. Digital twins excel at this, offering real-time asset health insights and forecasting future breakdowns.

Real-time data: Information delivered immediately after collection. Digital twins rely on real-time data from sensors to accurately reflect the current state of the physical system they represent.

Sensing technologies: Devices or tools that detect events or changes. These are crucial for digital twins, as they inform the twin of changes in the physical system, allowing it to mirror them accurately.

Simulation: Creating models for learning or problem-solving. Digital twin technology uses simulation to replicate, test and predict various scenarios.

Smart manufacturing: The use of advanced technologies to make production more efficient. Digital twins are key in smart manufacturing, helping predict machine problems, enhance learning and improve operations.

Urban digital twin: A comprehensive virtual model of a city, enhanced with real-time data from Internet of Things devices and advanced analytics using artificial intelligence.

Virtual commissioning: Simulating a physical manufacturing system in a digital twin format to evaluate and modify production plans.

Virtual reality (VR): A technology that creates a simulated environment, allowing users to interact with a digital 3D world. In digital twins, VR enables immersive interaction, enhancing understanding and decision-making through realistic simulations and visualisations.