What is a digital twin?

The evolution of digital twins

The concept of digital twins originated in the early 2000s, introduced by Dr. Michael Grieves at the University of Michigan. Initially applied in manufacturing and product lifecycle management, digital twins have since expanded across industries, from energy and resources to healthcare and smart cities. The advancement of key technologies such as information management, IoT, cloud computing, artificial intelligence, data analytics, and cybersecurity has accelerated the ability to scale digital twins and their specializations at the equipment or facility levels.

How digital twins work?

Digital twins function through a continuous data exchange between the physical and digital worlds. Sensors and IoT devices collect real-time data from physical objects or processes, a key information source for digital twins. Other information sources include, but are not limited to, asset documentation (for example, engineering drawings, equipment manuals, operations instructions, SOPs), business network information exchange (for example, track and trace the arrival of spare parts, replacement equipment, field service), geographical information systems, maintenance systems of record, financial systems of record, and much more. The digital twin processes this information, applies analytics or simulation models, and provides insights that can be used to optimize the physical counterpart's performance as well as the performance of teams directly or indirectly supporting the physical object, business process, or system. In the digital twin journey, a bidirectional relationship creates a feedback loop where the physical entity informs the digital model, and the digital model helps improve the physical entity by direct or indirect means through human action. Business value can be generated throughout this journey through trusted information, autonomous information, and AI and security embedded everywhere. The journey towards creating digital twins should be in lockstep with an organization’s information management journey.

The core components of a digital twin typically include:

1. Physical asset or process: The real-world object or system being replicated
2. Sensors and connectivity: Devices that collect data from the physical entity
3. Data storage and processing: Systems that manage and analyze the collected information
4. Visualization and interface: Tools that present insights and enable interaction with the digital twin
5. Analytics and AI: Capabilities that transform data into actionable insights and predictions

Components three through five above are integral to the information management software technology domain.

Different types of digital twin technology

Digital twins vary in complexity and application, with several distinct categories serving different purposes:

Parts-level digital twins

Also known as discrete digital twins, these focus on individual components or parts within a larger system. They capture information about a specific part's operation, performance metrics, and characteristics. Parts-level twins are particularly valuable in manufacturing and product design, where understanding the behavior of individual components is crucial. For example, a digital twin of a turbine blade can help engineers analyze its performance under various conditions without physical testing, saving time and resources while optimizing design.

Asset-level digital twins

Asset-level twins combine multiple parts-level twins to provide a holistic view of an entire asset, such as a machine, vehicle, or building. They help organizations understand how different components interact and affect overall performance. These twins are essential for comprehensive asset management, enabling predictive maintenance schedules based on actual usage patterns rather than predetermined intervals. For instance, an asset-level digital twin of a wind turbine can integrate data from all components to optimize energy production and reduce downtime.

System-level digital twins

System-level twins represent entire systems or networks of interconnected assets, such as a factory floor, an industrial plant, power grid, or transportation network. They model complex interactions between multiple assets and their environment, providing insights into system-wide performance and identifying optimization opportunities. These twins are increasingly being applied to human beings in use cases related to healthcare, financial management, and customer experience, creating personalized models that reflect individual behaviors and needs.

Enterprise-level digital twins

Enterprise-level twins connect system-level twins to deliver a comprehensive view across an organization's operations. They focus on monitoring and optimizing high-level business activities and outcomes, integrating data from various departments and processes. Enterprise twins help leadership teams make informed strategic decisions by simulating the potential impact of changes before implementation, reducing risk and improving resource allocation.

Process-level digital twins

Process-level twins model end-to-end business processes and workflows, focusing on relevant enterprise applications to build complete process models. These twins enable organizations to monitor and optimize cross-enterprise and cross-ecosystem processes, such as supply chain management, customer experience, and financial operations. By simulating process changes and their potential impacts, organizations can identify inefficiencies and implement improvements with confidence.

The three dimensions of a product digital twin

Product digital twins typically encompass three key dimensions that provide a comprehensive understanding of the product throughout its lifecycle:

Physical dimension

The physical dimension represents the product's material properties, geometry, and structure. It captures the tangible aspects of the product, including its components, connections, and physical characteristics. This dimension serves as the foundation for the digital twin, providing the basic framework upon which other dimensions build. Advanced technologies like 3D scanning, photogrammetry, and computer-aided design (CAD) are used to create accurate physical representations that serve as the basis for simulations and testing.

Behavioral dimension

The behavioral dimension models how the product functions and performs under various conditions. It includes operational parameters, performance metrics, and response characteristics that define how the product behaves in real-world scenarios. This dimension enables organizations to simulate product performance across different environments and usage patterns, identifying potential issues before they occur in the physical product. The behavioral dimension often incorporates physics-based modeling, mathematical algorithms, and historical performance data to predict future behavior.

Contextual dimension

The contextual dimension incorporates the product's environment, user interactions, and external factors that influence its operation. It considers elements such as usage patterns, environmental conditions, regulatory requirements, and market trends that affect the product's performance and lifecycle. This dimension provides crucial context for understanding how the product functions in real-world situations and how external factors impact its performance and longevity. Contextual information helps organizations make more informed decisions about product improvements, maintenance schedules, and retirement timing.

What is a supply chain digital twin?

A supply chain digital twin is a detailed simulation model that replicates an organization's entire supply chain ecosystem, from raw material sourcing to final product delivery. It incorporates real-time data from each step in the supply chain, including supplier information, inventory levels, production schedules, transportation logistics, and demand patterns. By creating a comprehensive virtual replica of the supply chain, organizations can visualize, monitor, and optimize their operations with unprecedented precision. Supply chain digital twins enable organizations to:

• Predict potential disruptions and develop contingency plans.
• Optimize inventory levels across multiple locations.
• Simulate different scenarios to identify the most efficient logistics strategies.
• Reduce waste and improve sustainability throughout the supply chain.
• Enhance transparency and traceability across the entire network.
• Improve collaboration with suppliers and partners through shared insights.

According to IDC, by 2023, 30% of manufacturers will enhance their shop floor digital twin with real-time signal transponder data, leading to an 80% reduction in logistic bottlenecks in shop floor and storage areas. The integration of supply chain digital twins with B2B collaborative network platforms supports ecosystems that enable agility, control, and resilience while providing digital connectivity to coordinate physical activities across networks.

Other types of digital twins and their value

Beyond the common categories, specialized digital twins are emerging to address specific industry needs and create unique value:

Industrial digital twins

Industrial digital twins are virtual replicas of physical industrial systems, such as machinery, production lines, or entire facilities. They integrate real-time data from sensors, IoT devices, and operational systems to mirror the physical asset’s behavior, performance, and condition. Used in industries like manufacturing, energy, and other industrial facilities, digital twins enable predictive maintenance, process optimization, and scenario simulation, improving efficiency and reducing costs. By leveraging AI, machine learning, and analytics, they provide actionable insights, enhance decision-making, and support innovation, all while minimizing downtime and risks in the physical environment.

Customer digital twins

Customer digital twins model individual customer behaviors, preferences, and interactions to create personalized experiences and offerings. These twins analyze historical data, current behaviors, and predictive models to anticipate customer needs and optimize engagement strategies. Financial institutions are increasingly using customer digital twins to model preferences and behavior patterns, allowing them to better target and customize products and services. According to some estimates, a human digital twin can be created in as little as two weeks and can significantly improve customer satisfaction and loyalty while increasing revenue opportunities.

Smart city digital twins

Smart city twins integrate data from various urban systems—transportation, energy, water, waste management, and public services—to create comprehensive models of urban environments. These twins help city planners and managers optimize resource allocation, improve service delivery, and enhance quality of life for residents. They can simulate the impact of new infrastructure projects, policy changes, or emergency scenarios, providing valuable insights for decision-makers. For example, digital twins have been used to model traffic patterns and optimize signal timing, reducing congestion and emissions in urban areas.

Healthcare digital twins

Healthcare twins model patient physiology, treatment responses, and healthcare systems to improve patient outcomes and operational efficiency. Patient-specific digital twins can simulate how individuals might respond to different treatments, enabling personalized medicine approaches. At a systems level, healthcare twins can optimize hospital operations, resource allocation, and care pathways. These twins are particularly valuable in chronic disease management, surgical planning, and medical device development, where they can reduce risks and improve outcomes through accurate simulations.

Digital twins reimagined: The OpenText approach

OpenText reimagines digital twins by sitting at the center of connected ecosystems and the internet of clouds. We play a critical role as organizations adopt cloud, security, and AI, all of which are key technologies in the creation and management of digital twins. Our digital twin framework contains key information management components that act as horizontal threads across the digital fabric, creating trusted, autonomous, and secure digital representations of machines, processes, and systems throughout their lifecycle. Here are some of the solutions OpenText offers:

Content management solutions

OpenText™ Content Cloud manages critical documentation related to assets, including equipment manuals, product data sheets, safety manuals, work orders, and installation images. This ensures that all documentation related to physical assets is properly organized, accessible, and maintained throughout the asset lifecycle, providing crucial context for digital twins.

AI content management

OpenText™ Content Aviator powers work with AI content management and serves as an intelligent AI assistant to quickly find answers to questions contained in asset documentation. This capability significantly improves the speed and accuracy of information retrieval, enabling project, engineering, operations, and maintenance teams to access critical information about assets represented in digital twins without extensive manual searching.

Supply chain automation

OpenText™ Business Network Cloud enables secure sharing of machine sensor and B2B data exchange between asset owners, manufacturers, and field service providers. No one should have to pick up a phone, send an email, or search for tracking numbers to determine when a critical part or service is expected to arrive. Digital twins have complete and frictionless insight into B2B transactional information related to the physical asset. This facilitates supply chain automation and helps predict failures before they occur, enhancing the predictive capabilities of digital twins across organizational boundaries.

AI for supply chain

OpenText™ Business Network Aviator provides insights with generative AI for supply chains, acting as a virtual advisor to quickly access information about when spare parts, replacements, or service will arrive. It can also surface other information about vendor transactions that impact machinery, improving the responsiveness and resilience of supply chains represented in digital twins.

Analytics solutions

OpenText™ Analytics Cloud enriches asset documentation, analyzes asset imagery for hazardous conditions, provides big data analytics for predictive maintenance, and more use cases. These capabilities enhance the analytical power of digital twins, enabling deeper insights into asset behavior and performance patterns.

Generative AI for analytics

The OpenText™ Aviator Platform tackles digital twin analytics challenge at scale. This platform leverages 150+ connectors to connect to various data sources that contribute to the creation of digital twins. An analytics platform optimized for speed and scaling is used to identify patterns and predict outcomes that might not be apparent through conventional analysis, adding significant value to digital twin implementations.

Customer experience management

OpenText™ Experience Cloud elevates discussions around assets via drone videos, technical support call quality management, and crowdsourced information for distributed assets. This solution connects the human experience dimension to digital twins, providing additional context and insights from users and operators.

Generative AI for customer experience

OpenText™ Experience Aviator elevates communications and experiences for success by creating tailored content to inform customers, partners, or other stakeholders about asset performance. This capability helps translate digital twin insights into actionable communications that improve operational outcomes.

IT operations management

OpenText™ Observability and Service Management Cloud provides service management and network operations management to speed up device monitoring, configuration, and resolution time. This solution ensures that the IT infrastructure supporting digital twins is reliable, secure, and performing optimally.

Generative AI for service management

OpenText™ Service Management Aviator empowers users, service agents, and IT staff to find the answers they need through a virtual agent for quick issue resolution, leveraging knowledge from service tickets on similar devices. This reduces resolution time and improves service quality for issues identified through digital twin monitoring.

Cybersecurity solutions

OpenText™ Cybersecurity Cloud defends against sophisticated cyberattacks on energy and resource infrastructure, ensuring that digital twins and the systems they connect to remain secure and protected from threats. This is particularly critical as digital twins often provide access to operational technology systems that could be vulnerable to attacks.

Generative AI for cybersecurity

OpenText™ Cybersecurity Aviator improves security posture with AI cybersecurity and agile threat detection, offering behavioral-based cyber threat hunting and detection. This solution helps identify and mitigate security risks that could compromise digital twin integrity or the systems they monitor.

DevOps solutions

OpenText™ DevOps Cloud streamlines the deployment of software that enhances digital twin creation and representation of assets. This ensures that digital twin applications are developed, tested, and deployed efficiently, with appropriate quality controls and continuous improvement.

Generative AI for DevOps

OpenText™ DevOps Aviator enables faster application delivery, development, and automated software testing to improve the quality, reliability, and scaling of digital twins. This solution accelerates the development lifecycle for digital twin applications, ensuring they remain current, robust, and aligned with business needs.

Information management approach

OpenText's information management (IM) approach to digital twin implementations involves product-level partnerships with solutions providers such as SAP, Microsoft, and Salesforce. These integrations streamline processes, improve collaboration, and enhance information governance. Using an IM system to unite information silos is critical to successfully implementing digital twin solutions that deliver maximum value throughout asset lifecycles.

Organizations already using digital twins

Digital twin technology has been adopted across various industries, with the following notable adoption trends and implementations:

Manufacturing and industrial

According to Gartner, half of major enterprises expect to be using digital twins by 2023, and a third of mid-to-large-size companies will have implemented at least one digital twin associated with a COVID-19-motivated use case. Within manufacturing specifically, by 2023, 30% of manufacturers will enhance their shop floor digital twin with real-time signal transponder data, leading to an 80% reduction in logistic bottlenecks in shop floor and storage areas.

Energy and utilities

In the energy and resources sector, digital twins are increasingly critical due to the capital-intensive nature of these industries. With a ratio of gross plant, property, and equipment to employees approximately 10 times higher than other industries ($2,000,000 of gross PP&E per employee), these companies are leveraging digital twins to manage complex infrastructure. The UK's National Underground Asset Register (NUAR) Pilot Programme serves as an example where 42 asset owners have participated to create a digital twin mapping 1.5 million kilometers of underground services, with an estimated financial value of £245 million per year.

Smart buildings

Amsterdam's "The Edge" is known as one of the world's smartest buildings, using nearly 30,000 sensors that feed user-generated content into a "data lake" to help operators manage their cutting-edge asset. The data allows operations and maintenance managers to predict which areas will require the most upkeep and resources. While advanced, analysts at the Centre for Digital Built Britain note that even this implementation could benefit from better building information modelling (BIM) integration to better handle the massive amount of data generated.

Cross-industry adoption

According to estimates, we can expect to see 65 percent of global manufacturers save 10 percent of their operational expenses through the use of digital twins. Organizations implementing digital twins typically reduce maintenance costs by 10-40% and decrease equipment downtime by 35-50%. According to a recent report from EY, using accurate digital twins can deliver a 35% increase in maintenance and operation efficiency.

How companies are using digital twin technology

Organizations are implementing digital twins to address specific business challenges and create value in numerous ways:

Predictive maintenance and asset optimization

Digital twins monitor equipment conditions in real time, predicting maintenance needs before failures occur. This approach reduces downtime, extends asset lifespan, and optimizes maintenance schedules based on actual conditions rather than predetermined intervals. Companies implementing predictive maintenance through digital twins typically reduce maintenance costs by 10-40% and decrease equipment downtime by 35-50%.

Product design and development

Digital twins accelerate innovation by enabling virtual prototyping, testing, and validation without physical models. This capability reduces development time, lowers costs, and improves product quality through extensive simulation. According to McKinsey, digital twin technologies can increase revenue by up to 10%, accelerate time to market by as much as 50%, and deliver a 25% improvement in product quality.

Production planning and optimization

Digital twins simulate manufacturing processes to identify bottlenecks, optimize workflows, and improve resource utilization. This application enhances productivity, reduces waste, and increases operational agility in response to changing conditions. Manufacturers using digital twins for production optimization typically report efficiency improvements of 15-20% and waste reduction of 10-15%.

Quality control and compliance

Digital twins monitor production processes in real-time, identifying quality issues and ensuring compliance with regulatory requirements. This capability reduces defects, minimizes recalls, and ensures consistent product quality across production runs. Quality-focused digital twin implementations have reduced defect rates by up to 25% while streamlining compliance documentation.

Customer experience and service delivery

Digital twins model customer interactions and service delivery processes to enhance satisfaction and loyalty. This application personalizes experiences, optimizes service operations, and identifies improvement opportunities based on actual customer behaviors. Companies using digital twins for customer experience management typically see a 10-15% improvement in customer satisfaction scores and increased retention rates.

Supply chain resilience and optimization

Digital twins simulate supply chain scenarios to identify vulnerabilities, optimize logistics, and enhance end-to-end visibility. This application improves inventory management, reduces transportation costs, and builds resilience against disruptions. Organizations implementing supply chain digital twins have reported inventory reductions of 15-30% while maintaining or improving service levels and significantly enhancing their ability to respond to disruptions.

Intelligent assistants

Digital twins are more than digital representations of people, processes, and things. They’re virtual and intelligent assistants that humans can engage with, ask questions, and expect trusted and secure answers in return. For example, when was the last time you were calibrated? When was your last failure and what was the cause? What is your pressure rating?

Challenges in digital twin development and implementation

Despite their significant benefits, organizations face several challenges when developing and implementing digital twins:

Data quality and integration

Digital twins require high-quality, consistent data from multiple sources, which can be difficult to obtain and integrate, especially in organizations with fragmented systems and inconsistent data practices. Poor data quality can lead to inaccurate models and unreliable insights, undermining the value of the digital twin. Organizations must develop robust data governance frameworks, standardization processes, and integration capabilities to ensure their digital twins accurately reflect physical reality.

Technical complexity and expertise

Building and maintaining digital twins requires specialized expertise across multiple domains, including data science, modeling, domain-specific knowledge, and software development. Many organizations struggle to assemble teams with the necessary skills or find partners with appropriate capabilities. Investing in training programs for a basic comprehension of information management, establishing centers of excellence, and forming strategic partnerships with technology providers can help address these expertise gaps.

Scalability and system architecture

As digital twins grow in complexity and scope, organizations face challenges in designing scalable architectures that can handle increasing data volumes and computational demands. Moving from pilot projects to enterprise-wide implementations often requires significant infrastructure investments and architectural revisions. Cloud-based platforms, modular design approaches, and well-defined scaling strategies are essential for managing growth in digital twin implementations.

Security and privacy concerns

Digital twins collect and process sensitive operational data that could pose security risks if compromised. Organizations must implement robust security measures to protect both the twins and the systems they connect to. Additionally, when digital twins incorporate personal data (such as in customer or healthcare applications), privacy concerns must be carefully addressed. Comprehensive security frameworks, regular audits, and privacy-by-design principles should be integrated into all digital twin initiatives.

Return on investment justification

Digital twin initiatives often require significant upfront investment before delivering measurable returns, making it challenging to secure budget approvals and stakeholder support. Organizations must develop clear business cases, establish appropriate metrics, and identify quick wins that demonstrate value early in the implementation process. Phased approaches that begin with high-value use cases can help build momentum and support for broader digital twin deployments.

Cultural and organizational barriers

Successfully implementing digital twins often requires significant changes to work processes, decision-making approaches, and organizational structures. Resistance to these changes can undermine adoption and limit the value realized from digital twin investments. Change management strategies, executive sponsorship, and user-centered design approaches are critical for overcoming these barriers and fostering a culture that embraces digital transformation.

Conclusion

Digital twins represent a fundamental shift in how organizations understand, manage, and optimize their physical assets, processes, and systems. By creating dynamic, data-driven virtual replicas, organizations gain unprecedented insights, improve operational efficiency, reduce costs, and drive innovation across their operations. As digital twin technology continues to evolve and integrate with advances in AI, IoT, and data analytics, its applications and value will expand further, making it an essential capability for organizations seeking competitive advantage. By reimagining digital twins as comprehensive information fabrics that connect across systems and processes, organizations can create scalable, intelligent representations that significantly improve operational performance.

While implementing digital twins presents challenges, particularly around data quality, expertise, and organizational change, the potential benefits far outweigh these obstacles. Organizations that successfully mitigate these challenges with information management technologies and best practices will be better positioned to adapt to changing conditions, optimize performance, and create sustainable value for all stakeholders.

As digital twins mature and becomes more accessible, they will increasingly become not just a competitive advantage but a fundamental requirement for operational excellence across industries, especially in asset-intensive sectors. The digital twin journey represents a significant transformation, but one that promises substantial returns in efficiency, innovation, and sustainability for organizations willing to embrace this powerful approach to connecting the physical and digital worlds.