The Digital Twin

The progress of communications, processing, storing, and sensing capabilities is increasingly enabling the Digital Twin approach as a means towards digital transformation. Different vertical industries are more and more implementing solutions that are Digital Twins or are inspired to it. These implementations are demonstrating how to move from a physical world into higher and more convenient levels of softwarization. It is important to describe what is the Digital Twin and why it is important as a technological solutions and as a business enabler. This chapter introduces some of the current trends in the usage of the Digital Twin, and what actors should be interested in its applications. The usage of the Digital Twin requires a considerable technological platform and related skills for a succesful implementation and usage. As such, there are risks and best practices to consider in order to realize the expected benefits of this approach. The chapter offers a perspective view on how to build Digital Twin based solutions and what are the steps to bring the Digital Twin in the mainstream of many industries.

Digital Twin adoption has reached an inflection point where their growth is now exponential. They will affect every individual and every enterprise in ways that are predictable and in ways that are unexpected. For an enterprise, their existence and use will affect all stakeholders: changing customers experiences, disrupting established business models, and transforming an enterprise’s operations, sales & marketing, R&D and innovation, strategy, governance, and everything that affects an enterprise’s success. Consider the following: The Digital Twin supply chain is rapidly expanding and evolving. Digital Twins are becoming much easier to create, use and integrate with startups playing a major role. The combination of digital twins and artificial intelligence is creating complex and dynamic twins that ‘understand’ the world in ways that humans alone cannot comprehend. Intelligent digital twins will self-modify and evolve independent of human intervention. The outcomes will be unexpected and significant. These intelligent Digital Twins will disrupt and transform every existing business model and make new ones possible that create new types of value. A digital first future could eventually emerge in which virtual Digital Twin and Metaverse assets are the primary source of value, and the physical/real world is secondary. This chapter discusses how Digital Twins, and the digital twin ecosystem, changes what we know about the creation of value. It discusses the changes that digital twins cause in value propositions, business models, artifacts, experiences and how enterprises and individuals influence each other. It covers how Digital Twins can transform markets and marketplaces, channels and supply chains, offerings, and operations. More importantly, this chapter discusses a future in which the relationship between the digital and physical worlds will fundamentally alter the value that individuals and enterprises create and consume.

The market for digital twins is promising, as interest in this transformative technology continues to grow. However, research shows that actual use of digital twins is sparse, with adoption rates struggling to surpass 10% in most industries. Arguably the biggest reason for this lack of traction is a similar lack of sufficient digitization across companies and industries. The fact is, organizations’ slow pace in digital transformation means the digital foundation necessary for digital twins to thrive is still lacking in most companies and industries, although front-runners and laggards are beginning to emerge. Simply put, digital maturity must increase substantially for companies to fully benefit from them. When this happens, digital twin adoption should accelerate rapidly, and companies will begin to see how digital twins can play a crucial role in their efforts to create greater operational resilience; optimize supply chain networks, processes, and inventory; and foster bigger strides toward sustainability.

The Digital Twin (DT) is a concept introduced at the beginning of the twenty-first century but did not gain traction until the middle of the last decade. It is a concept that was first adopted for tangible industrial products and has since expanded to all manner of products and services. This includes not only application to inanimate entities but also to the biological conditions of people. Digital Twins are rapidly moving into the intangible realm of processes and abstract ideas. The Digital Twin consists of different types to be a framework for the entire lifecycle of the entities. While the Digital Twin today is a concept that is created by its users, the prediction is that its evolution is to be an intelligent platform. This will allow work to move from the physical world into the virtual world with major impacts on efficiency and effectiveness.

This chapter focuses on giving an overview about Digital Twin architectures. It emphasizes the description of a general architecture of a Digital Twin system based on different usage scenarios. These usage scenarios are following the evolution path of Digital Twins, staring from Digital Twin for production lifecycle management (PLM). From this starting point, Digital Twins are used with 3D visualization (AR/VR) and product usage simulations. Then Internet-of-Things (IoT) technology enabled capturing the dynamic state of the real asset. In todays closly connected world, cloud-based Digital Twins are utilizing broadband networks to enhance the real asset with cloud-based functionalities. The emergence of data spaces (such as define in GAIA-X or IDSA) enables secure and trusted data sharing. This will be the basis for distributed Digital Twin Worlds that simulate large parts of the world and solve important problems like the decarbonisation of the global society. Each step in this journey has an abstract architecture and a concrete example how the architecture is used.

The application of Digital Twins (DTs) has gained traction across industrial and manufacturing organizations. The adoption and implementation of Digital Twins invariable requires the business case to pass muster before the allocation of resources to a specific project. The bar is even higher if Digital Twins are to be adopted and encouraged within an enterprise as a way of doing business and as a way of thinking about complex problems. For individual projects within an organization, it is crucial to access technologies and methodologies that repeatedly have a high probability of achieving success. This includes adoption of a general framework where the organization can gain confidence from learnings across projects and to develop trusted general processes and tools that lift the level of practice across the organization over time. The Composable Digital Twin is such an approach and has at its core several important features. It offers re-use of effort, accelerated time to results, general applicability, and the dynamic range to address both simple and complex issues within an enterprise at scale. The building of enterprise capabilities and the development of project specific Digital Twins also requires processes management techniques that minimize risk and build confidence. The Lean Digital Twin exploits the idea of a minimum viable product and lean and agile development techniques for managing DT development projects. It provides a set of steps for guiding Digital Twin projects while aligning business goals and outcomes with technical capabilities across a project’s lifecycle. It also emphasizes the accomplishment of early results. The Chapter explores and illustrates the importance of both ideas, composable and lean DTs, with step by step descriptions and fielded examples.

Saturated markets and the continued need to lower costs drive the evolution of modular and networked Smart Factories also in reply to the trend towards an individualized customer demand driven mass-production. The accelerating digitalization of industries support the migration from Industry 3.0 which focuses on automation to Industry 4.0 where digital twins of both, the product and production itself enable this paradigm shift towards a networked production.Digital twin representations have evolved from definitions of the German Platform “Industry 4.0” since 2017 starting from asset administration shells (AAS) that describe services being offered from assets like products, machines, sensors and software components. Together with OPC-UA as future standard, asset administration shells allow vendor independency and active networking in product design and manufacturing.This enables a new degree of interoperability and thus facilitates the new paradigm of networked production embracing manufacturing across different enterprises. This has the potential to drive outsourcing and just-in-time supply chains to a new level with disruptive implications for conventional product provisioning. Virtual factories could be designed by production elements acquired in a highly dynamic platform-based economy.In addition to this fundamental shift, the technology progress of wireless connectivity results in new interfaces for 5G networks in vertical industries offering deterministic and reliable ad-hoc connectivity of people, machines and factories where needed. The network itself will be treated as an asset in near future being described and managed by an AAS. The digital twin of 5G user ends and network gears will help to forecast potential network loads in given industrial applications and to negotiate the quality of service parameters needed in the actual application, i.e. latency and reliability of a 5G connection.Other recent international initiatives related to trustworthiness focus on the development of trusted architectures in these networked production settings which will further push the evolution of digital twins. Finally, emerging auction-based digital business models enable a new monetarization of services and the negotiation of costs of production under consideration of new aspects such as environmental constraints, CO2 footprints and corresponding additional costs resulting of i.e. energy, waste, and resources supporting sustainability in a circular economy.Thus, digital twins break with traditional paradigms and open up amazing opportunities in the engineering phase of collaborative production schemes. All this will be further supported by artificial intelligence services and powerful Cloud-Edge infrastructures that will help to match the technological, business and ecological requirements and to mitigate between service requester and supplier in a platform economy as a valuable first step in reaching climate neutrality per design.

With the raise of Internet of Things (IoT), the Digital Twin (DT) concept came across newfound lifeblood. The rapidly growing volume and breadth of data that can be captured, processed and forwarded by the smart devices through IoT-related technology, indeed, represent a key enabling factor for making DTs finally ready for prime time, beyond the bounded confines of manufacturing domain. Conversely, the value that DTs add to the management, development and commercialization of IoT systems can have a disruptive impact in the whole ICT landscape of the next few years, further bridging the physical-virtual divide. Such, promising yet challenging, synergy is the topic of this Chapter, in which both conceptual and practical solutions for the integration between DT and IoT are presented as well as the state-of-the-art of main DT-aided IoT Platforms reviewed.

Companies in all industries are facing a future of tougher competition, exacting product requirements, and growing complexity. Arguably the most disruptive trend, this growing complexity manifests itself in several ways. Products are becoming smarter and more sophisticated, with components and subsystems being sourced from multiple domains. At the same time, manufacturing systems are also growing in complexity as they become more efficient, flexible, and connected to produce advanced products and keep pace with the rapid cycles of today’s industry. As companies attempt to adapt to the challenge of growing complexity, they must also balance the operation of a successful business today.The key to managing this high-wire act is digital transformation. Digital transformation, or the widespread digitalization of processes, data flows and methodologies, provides a holistic data-centric view of the world instead of being limited to application or domain-centric silos of information. Digital transformation enables companies to manage complexity, integrating all parts of the business, to turn data into value at every stage of the product and production lifecycles: design, realize and optimize. At the heart of digital transformation is the Digital Twin, which accelerates digital transformation efforts and enables companies to design, build and optimize next-generation products faster and cheaper than ever. Furthermore, the Digital Twin can provide the structure to transform your company into a digital enterprise, unlocking new levels of process innovation and allowing companies to modernize their core business models.Those organizations that embrace digital transformation and the Digital Twin can turn the growing complexity of modern products and processes into a competitive advantage to outperform industry benchmarks. As complexity continues to grow, these capabilities will go beyond a competitive advantage to become a competitive necessity.

With the emergence of Industry 4.0, smart applications and systems are becoming the norm, transforming traditional industries and sectors towards an increasingly data-driven approach. Digital Twin technologies are one of the driving paradigms for the production, integration, sharing and reuse of data. In this context, understanding and mastering the description and formalisation of data as well as its proper, secure, compliant management are crucial for a large-scale adoption of the Digital Twin technology.We introduce the essential concepts for data, data structures and data management, and examine the importance of specific technologies, like ontologies, for the meaningful description of data and their relationships. We cover the essential modelling layers connected with data in this context, and present selected collaboration platforms for Digital Twin ecosystems in a context of interoperability as envisaged in the Digital Thread.

Models based on physics were the major protagonists of the Simulation Based Engineering Sciences during the last century. However, engineering is focusing the more and more on performances. Thus, the new engineering must conciliate two usually opposite requirements: fast and accurate. With the irruption of data, and the technologies for efficiently manipulating it, in particular artificial intelligence and machine learning, data serves to enrich physics-based models, and the last allows data becoming smarter. When combined, physics-based and data-driven models, within the concept of Hybrid Twin, real-time predictions are possible while ensuring the highest accuracy. This chapter introduces the Hybrid Twin concept, with the associated technologies, applications and business model.

This chapter addresses the value and the synergies of combining Artificial Intelligence technologies with Digital Twins. We begin with a high level, but comprehensive review of AI technologies that may be important for Digital Twins. Then we examine the relationship of AI techniques and methods to the progression of capabilities for Digital Twins in general. The properties and architecture of Digital Twins are then analyzed and related to major AI techniques. The objective is to identify those AI technologies that provide a unique advantage in the design and implementation of Digital Twins. The impact on the Digital Twin’s ability to meet end use requirements is illustrated through a simple use case. This points to the importance of including AI from the beginning in the design and construction of Digital Twins. In considering additional use cases we map how AI can be applied to Digital Twins in more complex situations. Finally, we provide general guidelines for inclusion of AI to be incorporated during the design phase of a Digital Twin. This includes the use of AI within the Digital Twin itself and in operations where the context is the larger system that the Digital Twin supports.

The intent of this chapter is to demonstrate the value of a transversal (i.e., cross-verticals and multi-actor) digital twin platform for Internet of Things (IoT) applications and complex cyber-physical systems at large (e.g., large-scale infrastructures such as telecommunication or electricity distribution networks) around the Thing in The Future experimental digital twin platform developed at Orange. Several real-life illustrative use cases in various domains — smart building, smart factory, smart city, and telecommunication infrastructures — developed by Orange and partners, are introduced. Main design, architectural and technological choices, which sustain this ambition, are discussed: graph-based structural and semantic modelling of systems of systems, large scale graph storage, platform distribution and federation.

Digital twin technology is poised to become an ubiquitous addition to the global technology landscape. As with every other technology or computing capability, platform, environment or ecosystem, risk always accompanies benefit. In order to mitigate risk and effect robust, safe, secure computing environments and capabilities, one must first identify and comprehend the implications of unmodulated risk.Digital twin technology is no exception—in fact quite the contrary. As a consequence of digital twins being intrinsically associated with physical objects, the potential for negative outcomes is greater than for many other computing applications. Because digital twins will be employed in applications that interact with and control real-world, physical objects, they will also affect human beings who use or rely on those very real objects that are ubiquitous in our everyday physical world.This chapter discusses the specific areas of cybersecurity and dependability risk in digital twin environments and applications. Dependable systems must also be secure and safe. There is both interplay and interdependency between elements of dependable systems and elements of secure systems.The challenge of making systems dependable and secure is exacerbated in situations where components are physically more exposed and, therefore, potentially vulnerable to attack by external agents or entities. Such is the case for systems that employ digital twins—engendering a serious imperative to address cybersecurity and dependability. Neglecting to do so invites more serious consequences—not the least of which is harm to humans—as these systems involve physical, real-world objects with which human beings will interact.

The successful use and adoption of Digital Twins hinges on a general infrastructure comprised of at least four technology areas. The converged Networks for Data, Digital Storage, Computing, and Communications form the necessary fabric to host and operate Digital Twins. This combination promises to deliver both the functionality and intrinsic attributes that make good on the promises of Digital Transformation. They are what makes it possible to conquer and tame the complexity of the barriers to successful management of the lifecycles for manufacturing, products, services, and processes. There are two concepts that are important here: the first is the decoupling between the infrastructure itself and the applications (e.g., Digital Twins) that run over it; and the second is infrastructure resources that are software defined, distributed, composable, and networked, to fit a large range of applications. In this Chapter we motivate the characteristics of the end-to-end data, storage, computing, and communications fabric which will ideally host Digital Twin models as they are built, deployed, operated, and continually refined. We further address the converged infrastructure that connects the physical endpoints, involving both humans and machines, to the digital space existing in Clouds, Edges and High-Performance Computing Centers. Specifically, we focus on the role of the critical boundary between physical systems and cyber space, and between Operational Technologies (OT) and Information Technologies (IT), where a challenging cultural and technological transition needs to fully unfold. The last point is illustrated by examining the application of Digital Twins in two critical domains, modern manufacturing and automotive.

The current 5th Generation Mobile Networks (5G) standardization is aiming to significantly raise the applicability of communication networks for a wide variety of use cases spanning from industrial networks, automotive, content acquisition, multimedia broadcasters and eHealth (NGMN Alliance. 5G white paper. Next generation mobile networks, white paper 1, 2015). At the same time, this presumes that a smaller size, dedicated 5G network must be integrated into an existing complex communication infrastructure, specific to the use case. This becomes particularly challenging with a 5G network as it is a highly complex systems by itself with highly complex network management requirements in terms of fault, performance, and security. To address this issue, existing work suggests that the use of Digital Twins (DT) or Asset Administration Shells (AAS) within the industrial domain, to model information about the 5G network and to use this data to plan, evaluate and make decisions on how to optimize the behavior of the system. However, the DT based modelling of 5G systems remains a relative new topic. Within this chapter, we provide a comprehensive overview of how the exiting 5G network management uses a sort of Digital Twin (DT) approach and how a full DT paradigm would optimize the 5G networks. First, the 5G network as a complex system will be described with the specific automation and optimization capabilities as well as underlining its limitations. The additional opportunities for a more flexible DT of the 5G network, due to its softwarization would be further analyzed especially concentrating on the extension of the DT model towards an even more complexity as well as towards the new opportunities of dynamic resource scheduling as representative elements for the 5G network management functionality. A short analysis on the impact of the network between the DT and the 5G system will be provided to understand the impact of the network characteristics such as delay, capacity, and packet loss on the functioning of the system. To conclude, the presented considerations can act as robust enablers for future 6G networks including multiple self-reconfiguration mechanisms. A short set of considerations are made on the governance of the multiple decision points and potential ways to implement such multi-decision models.

This chapter provides an overview of Augmented Reality (AR) as a training tool in manufacturing sectors, with a focus on manual assembly procedures. The proposed analysis investigates the two main components of an AR training system, the content creation or expertise capture (i.e., authoring) and the content consumption or information conveyance (i.e., training), separately, as they are generally treated in the literature. Finally, we present a classification of information conveyance mediums in AR, a relevant topic particularly for AR usage in industrial context.

The 4th Industrial Revolution is improving the existing industry’s paradigm in a radical way by digitally transforming everything, including life and business, using digital technology. The digital twin technology, which forms the core of such a digital transformation, provides benefits such as optimal operation and cost reduction by implementing the real world in a virtual space and analyzing, controlling, and simulating. Digital twins are now being applied to all industries, starting with manufacturing, city, energy, agriculture, and healthcare. As digital twins, which started in manufacturing, spread to all areas of the industry, interoperability is emerging as an important issue.To provide interoperability in digital twins, various global standards development organizations (SDOs) have come up with specifications related to the digital twin. For example, the 3rd Generation Partnership Project (3GPP) is developing standards for 5G, which can provide high-speed and reliable communication functions required for digital twins, and the Open Geospatial Consortium (OCG) manages geospatial information used in smart cities and other domains. ISO/TC 184 covers industrial data standards used in various domains such as manufacturing, industrial automation, and information systems. Furthermore, oneM2M, a global initiative to standardize a service layer IoT platform, defines common service functions for digital twins.In this chapter, in particular, ISO/TC 184 for industrial data in the smart factory field, ISO/IEEE for the digital health data, IEC TC65 for the interoperability in the smart factory, and oneM2M for service function for digital twin services are introduced. In addition, open-source activities in the domain of digital twins are gradually expanding in particular for digital twin platforms and data management. In addition to this, we look at several best practices of digital twins around the world.

This Chapter provides an overview of Open Source, including the origination with specific references that trace the history of Open Source Code projects, including successes and failures.This approach gives the reader an understanding of an early concept of best practices, including the roles of the teams involved in the process and critical areas concerning the economic engineering of project consumption. Considerations spanning the development phase through the lifecycle of Open Source projects, both partners and projects for expanding the ecosystem, are explored. The Chapter concludes with a description of the Digital Twin Consortium’s Open Source Collaboration Community reference design platform. The reference architecture platform stack’s primary components are further detailed through representative Open Source Use Cases delineating the primary elements.

Digital Twins are an essential tool of Digital Transformation and are one of the enablers that allow us to deal with complex problems and complex systems. The technical foundations for Digital Twins start with deep domain knowledge in the area of application and fundamentally rely on information and operation technologies. A key ingredient is the unrelenting trend to recast stand-alone mechanical systems with digitally analogs where operations are data driven and managed through sophisticated digital control systems. The consequence is a remarkable explosion of performance improvements and new functionality. The enablers are the emergence of computing power that is inherently distributed and widely available, the virtualization of previously mechanical functions and processes through software, and the hyperconnectivity of organizations, the products, and devices they provide, and consumers. What is new and exciting is the use of Digital Twins to understand the dynamics and flows in complex systems by creating a Metaverse where it is possible to explore actions and decisions faster than real timeThe successful implementation strategy for Digital Twins relies just as much on human and organizational factors as it does on the technology. The important aspects of this are committed leadership, development of human capabilities, tools and infrastructure within the enterprise, increased reliance on collaboration within a greater ecosystem, and the realignment of corporate structures to take advantage of new business models and new capabilities. The Chapter illustrates these points in four different areas: supply chains, healthcare, manufacturing, and transportation. Because missteps are more than possible the Chapter also addresses the precautions to take in implementing Digital Twins. To be competitive and to thrive now and in tomorrow’s world Digital Twins are an important concept for organizations to internalize and an imperative to put into practice now.

This chapter explores the use of physics-based modeling & simulation technologies as part of Digital Twin solutions. It describes how an understanding of physics-based phenomena at microscale can be the basis for avoiding failures at a systems level, not only to improve safety, but to also reduce operating costs dramatically. The use of physics in the Digital Twin ecosystem enables the capability to see beyond the limitations of sensors and extends the ability to reasonably predict the future health state of complex mechanical systems.In mechanical systems under operation, components break when materials fail. Fortunately, the progression to failure can be modeled and anticipated using a physics-based approach to analyze the accumulation of damage accumulation of damage due to microstructure-level stresses microstructure level stresses and eventual initiation of component failure. This capability is critical to forecasting the ability of a mechanical design to withstand operational loading conditions and for predicting component stresses that will ultimately determine the remaining useful life of the system. It is also important for establishing inspection and remediation procedures to avoid failures.Integration of physics-based modeling offers a complementary prognostics capability that, combined with advanced sensor data analytics, can be used to predict the probability of failure of key components at any point in the lifecycle - even before failure initiation. The Chapter further describes a hierarchical multi-level framework that prescribes how detailed physics- based modeling can be extended to account for behaviors at a systems level. The approach is illustrated through a use case involving aerospace – specifically, helicopter drive systems – and addresses how the multiscale framework approach that combines physics and data science can maximize health state awareness in specific applications.

The Digital Twin concept is an all-encompassing Industrial Internet of Things (IIoT) use case. It is an artificially intelligent virtual replica of a real-life cyber-physical system (CPS) useful in all phases of a system’s lifecycle. The Digital Twin is made possible by advances in physics-based modeling and simulation, machine learning (especially, deep learning), virtual/augmented reality, robotics, ubiquitous connectivity, embedded smart sensors, cloud and edge computing, and the ability to crowd source the domain expertise. These technologies have the potential to make Digital Twins anticipate and respond to unforeseen situations, thereby making CPS resilient. To realize resilient CPS, engineers from multiple disciplines, organizations and geographic locations must collaboratively and cohesively work together to conceptualize, design, develop, integrate, manufacture, and operate such systems. The refrain “model once, adapt with data and domain expertise, and use it many times for many different purposes” offers an efficient and versatile approach to render organizational silos extinct. This is accomplished by providing a “single source of the truth” representation of the CPS, to collaborate virtually, assess and forecast in evolving situations, and make adaptive decisions. Organizationally, Digital Twins facilitate situational awareness and effective organizational decision-making through the acquisition, fusion, and transfer of the right models/knowledge/data from the right sources in the right context to the right stakeholder at the right time for the right purpose. That is, the design, manufacturing, optimal operation, monitoring, and proactive maintenance of the CPS.This Chapter addresses the operation of Digital Twins within an enterprise process. The discussion is generally applicable, but illustrated specifically with examples from the Aerospace Industry. It begins with a vision for the enterprise Digital Twin methodology to provide timely and accurate information created during the initial conceptual design, product development, and subsequent operational life cycle phases of the product/system utilizing a comprehensive networking of all related information. All related partners share such information, thereby connecting product/system design, production and usage data with those human and non-human agents requiring this information.The Chapter further reviews the enterprise-wide product lifecycle phases and describes how the use of digital twin methodology allows quasi-static model-based systems engineering (MBSE) and Enterprise Resource Planning (ERP) business models to morph into a temporal information continuum, spanning the life cycle of the product or system. Specifically, the focus is on global information flow throughout the enterprise, and suggested DT-committed organizational changes affecting the enterprise. It is followed by a discussion of MBSE-based requirements analysis and platform-based design principles in the product’s conceptual design phase and related examples. This lays the groundwork for encouraging a range of conceptual design ideas, standardizing design, analytical and learning tools for superior coordination and integration of the information flow within the enterprise’s Digital Twin processes.Subsequent portions of the chapter discuss the product development phase via Digital Twin models and platform-based design principles using digital 3-D CPS models with specific examples from Sikorsky and GE as illustrations. An emphasis is placed on the improvement of computing capabilities, such as the introduction of hyper-efficient Graphical Processing Unit (GPU)-based computational capability that provided an order of magnitude improvement in design productivity. Multi-functional causal models are introduced to help uncover failure modes, their propagation paths, and consequent functional effects, and discuss how such digital twin models automatically generate fault trees for risk assessment analysis, and an initial Failure Modes, Effects, and Criticality Analysis (FMECA) report. The importance of domain knowledge and data-informed models is emphasized in how it can aid in product testing, qualification, and certification phase. A formal system of health modeling to test the severity of candidate faults and their effects to generate an updated FMECA model using the digital twin is also introduced. This enables design engineers to understand the potential faults in the system, their probabilities of occurrence, and their manifestation as functional failures (effects), monitoring mechanisms for making the effects visible, system level implications in terms of safety, customer inconvenience and service/maintenance implications, and so on. The Digital Twin methodology enables the FMECA and fault tree updates in real-time.The role of Digital Twins in product manufacturing, quality management and distribution phase is addressed next. It includes a description of an integrated process for additive manufacturing and an advanced 3D quality inspection process. The DT network links these highly accurate coordinate measurement processes with the complex 3D Cyber-physical models intended to define the product accurately. As part of the Digital Twin, an integrated on-board and off-board system health management, coupled with virtual/augmented reality, can improve customer experience and support via real-time monitoring, incipient failure detection, root cause analysis, prognostics, predictive maintenance, and training assessment. The S-92 helicopter’s data integration process serves as a constructive example of how a proactive digital twin-aided health management system can significantly improve product resilience, safety, and customer acceptance.The remaining portions of the chapter describe how the Digital Twin infrastructure’s ability to process enormous amounts of data into information and knowledge, aided by the Failure Reporting, Analysis and Corrective Action System (FRACAS) database, enables proactive product configuration management and active learning. The result is efficient product maturation and customer adaptation. The Digital Twin’s ability to support the design of environmentally sustainable products and how they can eventually be suitably disposed is also addressed. The successful adoption of Digital Twins has other consequences, including the need to revamp traditional organizational structures to be effective in a globalized environment. While Digital Twins offer great promise, it is also important to consider some cautionary thoughts on the need for accurate models, domain knowledge-informed machine learning, and awareness of human fallacies in implementing the Digital Twin methodology. Finally, in a business environment, commitment to the Digital Twin methodology hinges on an understanding of the value that Digital Twins provide and the steps that the enterprise must take to successfully adopt the methodology. The enterprise must accept significant structural and cultural changes to succeed with a DT methodology. The authors firmly counsel that adaptation of these necessary enterprise modifications will not be easy and, as such, will require top-down leadership to respond to structural and cultural changes with the necessary corporate resources (adequate and digitally literate staff, leadership, hardware-software computing and communication infrastructure, and budget).

Looking at digital twins in terms of their information sets (master and shadow models), a significant part of the shadow models is created in the context of product life. Digital twins must be designed accordingly, focusing on their dedicated added value or business model. This concerns not only the information and data models, but also the communication technologies, processing routes and interaction mechanisms used. With appropriately designed digital twins, product life becomes a source of knowledge for optimizing or tracking product systems. MRO processes play a special role in this. Here, the digital twin becomes a monitoring system, information source, process manager or information sink through suitable functions and thus a potential knowledge repository.

The Digital Twin (DT) concept has rapidly gained acceptance. More recently it has become clear that just as a DTs support product, they can also represent the activities needed to design, execute, and manage projects. DTs bring a remarkable potential to bring complex projects to market successfully and to support after-market phases including training, maintenance, repair and retirement.Project Design is a method that brings DTs to project management based on realistic and reliable project models, forecasts, and ongoing instrumentation. In Project Design, the digital twin represents not only the products, services, and processes being created, but also the project teams and their activities. In other words, the project itself is recognized as a system. Digital models are extended to include people and organization in addition to product and process. Feedback and feedforward with automated flows are a critical characteristic of DTs leading to better attention, decisions, and actions by teams. Three cases are shown which demonstrate Project Design with digital models, digital projections, and digital shadows of complex projects.These cases show a collaborative environment in which teams build models which capture the project as a sociotechnical system. The models integrate three fundamental contributing architectures: products, processes, and organization (PPO). While building the model, a view emerges of the relationships amongst these three which, in turn, promotes shared awareness of the project across teams. The model-building likewise shapes mental models as teams explore the impact of changes, variation in assumptions, architectural options, and other real-world execution parameters.An analytics engine, in this case an agent-based simulator, generates project forecasts which act as digital projections. The forecasts are more realistic than classic methods, as the simulations include the project’s uncertain demands, behaviors, feasibility, and coordination in dynamic interaction. A wide range of feasible project variants with schedules, costs, quality, and utilization are generated by simulation and compared to targets. Teams rapidly assess trade-offs, risks, what-if scenarios and contingencies. The model is adjusted: teams expanded or reduced, dependencies changed, activities added or removed, roles and responsibilities tuned, concurrency increased, worksites changed, etc. The project teams learn quickly how changes in their own roles, commitments, and priorities systemically impact the project results. As the project proceeds, estimates to completion including alternate paths forward are rapidly and easily analyzed.A long-lasting benefit of Project Design is that the DT is used over the project lifetime. A digital shadow evolves with the actual project as refinements and contingencies arise, immediately yielding new forecasts of quality, schedule and cost. Instrumentation of scope, interfaces, and teamwork brings significant new feedback to maintain alignment of the project model with the actual project. The model also acts as digital thread across the model’s connected PPO and across changes in the project over time, promoting persistence for practical leverage of information in future projects.Recent research advances in instrumentation and analytics, including placement of non-intrusive sensors across project elements and teamwork, rapidly reveal the health of the project and chances for success. Taking advantage of these techniques, new insights are yielded on teamwork as innovation and complex problem-solving across various industrial and government project domains.

The Digital Twin concept has been used for many years in the oil and gas industry, typically supporting the simulation of data for training and planning. A more expansive advanced Digital Twin concept is now becoming the norm, providing owner-operators with a seamless integrated end-user experience. This advanced Digital Twin combines spatial information with schematics and static data, integrated with dynamic data from multiple systems of records.The advanced Digital Twin concept is particularly appealing for offshore owner-operators who are looking to reduce costs and improve safety. Reducing costs associated with the number of people offshore can only be done safely if there is better quality information available onshore. As well as conveying and visualizing information in a location- and context-aware manner, the advanced Digital Twin also supports the introduction of new technology such as virtual reality, augmented reality, mixed reality, and machine learning. These new technologies help provide better insights for owner-operators across the entire lifecycle of an offshore asset, from cost avoidance during construction, operational improvements during training, and support for integrity management and fabric maintenance during operation.There are different maturity levels for Digital Twins in offshore oil and gas. As technology continues to advance and owner-operators increase adoption there is the prospect of more mature Digital Twins that specify actions without human intervention. There is an opportunity to greatly streamline offshore operations, reducing costs, improving safety, and enabling rapid reaction to external conditions.

Manufacturers are realizing that the saying “anything is possible” isn’t just hyperbole. Today’s manufacturers are dealing with disruption and change in all forms. How do you know how your manufacturing and supply chain will respond? More importantly, how can you leverage technology and “gamification” to predict how the manufacturing and supply chain will behave if “anything happens”? The answer is through the use of Manufacturing Digital Twins. Digital Twins are not limited to the product anymore. Manufacturing and Supply Chain Digital Twins exist in many companies today and represent a wide arrange of scope and depth of detail. Technology advancements have enabled early stage manufacturing digital twins to evolve from being equipment focused to completely represent the factory, manufacturing processes, and even the supply chain. Manufacturing Digital Twins break down data and operational silos, provide holistic views, and enable multiple strategies to test different business scenarios – in a virtual environment and without impacting the physical production and order flow. Manufacturing Digital Twins are all different, based on the industry, manufacturing model, and supporting supply chain. Companies today are reaping the rewards for early and continued investment in Manufacturing Digital Twins; others are just starting the journey. Many lessons can be learned from the history of Manufacturing Digital Twin’s evolution, the impact of technology and computing power, and how company strategy and approach affects success – or failure. “Anything is possible” as Manufacturing Digital Twins and Supply Chain Digital Twins are here. Leaders are using them to differentiate, win and compete in today’s markets. This chapter explores the Manufacturing Digital Twin evolution, scope, key considerations, value, and benefits while providing guidance for you to succeed.

A mobility transformation is taking shape across multiple industries. The transformation is a combination of technological, regulatory, and societal changes all pushing for greater safety, sustainability, and equity in human mobility. In the automotive industry, this transformation has manifested in the immediate push for vehicle electrification, the continuous increase in automotive electronic and software content, and the continuing development of connected, automated and autonomous vehicle (AV) technology.This transformation is pushing the complexity of vehicle development to new levels in multiple aspects. Electrification, autonomy, and connectivity are driving requirements for more computing power, more electronic control units, and clever packaging of these many devices into the vehicle. Meanwhile, more and more features are being implemented through software in all types of vehicles, increasing software complexity and drawing additional focus during vehicle development. As each of these subsystems becomes more sophisticated, the task of integrating them into a robust, safe, and high-quality vehicle becomes even more difficult. The question for today, then, is how to continue to advance automotive design, features, and technologies to realize the potential of the future of mobility, despite the challenge of growing complexity.We believe a new approach to vehicle development is necessary. Automotive manufacturers must embrace digitalization and break down the boundaries that often exist between engineering domains and the stages of product development and manufacturing. Key to this approach is a comprehensive Digital Twin that captures every aspect of the vehicle design and production. Using such a Digital Twin, automotive manufacturers can connect engineering teams from across the electrical, electronic, software and mechanical domains. This means automotive manufacturers will be able to design, verify and validate entire vehicle platforms, ensuring the highest standards of safety, reliability, and passenger comfort.In this chapter, we will discuss the major trends of electrification and autonomy and examine how digitalization and the Digital Twin will be crucial to the creation of the advanced vehicles of the future. Though we focus on the consumer automotive market in this chapter, the benefits of digitalization and the Digital Twin are equally applicable to other vehicle manufacturing industries, such as heavy-equipment and off-highway vehicles.

Discussions of Digital Twins in ship design, construction, and operation pervade current industry literature. Although computer modeling has been used for many years to develop and analyze discrete ship products and processes, twenty-first century data management tool capability and capacity offer increased opportunity to create and examine complex shipboard and ship enterprise digital systems. Digital capability is now available for widely dispersed teams to collaborate on and demonstrate not only ship system and subsystem performance, but the efficacy of construction plans and sequences, the success of operational scenarios, and the prediction of requirements for maintenance activities. The 3D CAD models of the 1990s and early 2000s can now form the basis of comprehensive Digital Twins of entire shipbuilding fabrication and assembly facilities, and complete ship designs which can be used well beyond the design and construction phases into testing, training, operation, maintenance, and upgrade activities.This chapter presents a discussion of the current use and envisioned applications of Digital Twins in the various stages of ship design, planning, construction, operation, and eventual retirement. The entire ship life cycle is labeled the “Enterprise” and is subdivided into “Domains”, being the phases from earliest concept development through end-of-life retirement, including Concept Formulation, Design, Manufacturing, Operation, Maintenance and Disposal. Specific tasks, labelled “Use Cases”, comprise each Domain and include “Development”, “Verification”, “Work Instructions”, and “Collaboration”, as examples. The Domains and Use Cases are described along with the Digital Twins deployed and their enabling technologies.The chapter acknowledges and addresses the several barriers to Digital Twin implementation that have been encountered. Recommendations for prioritized deployment of digital tools and twins throughout the shipbuilding and ship-owning enterprise are offered. The types of twins and associated technologies enabling success are discussed and, most importantly, an implementation strategy and recommendations for industry leaders is presented.

Construction, the largest industry in the world, contributes 13% of the global GDP and is responsible for the infrastructure that supports the whole economy. It is troubling that this great industry faces decades of stagnant productivity and an alarming level of delays and budget overruns in the delivery of projects. Construction faced decades of growing construction-manufacturing gap in business performance. It all comes to one significant difference that explains this condition, the level of supply chain integration along the whole project process. Advanced digital technologies enabled an extraordinary level of business process integration. Construction continues to be the least digitized industry; its digitization level is a little above hunting and agriculture. The dominance of the project-centric approach to business is the main obstacle to construction business transformation. This document is about the way to overcome this obstacle.

Sustaining our cities by providing them with the tools to flourish is a key objective if we are to safeguard humankind and ensure that the places we live, work and play in remain resilient. The ecosystems that provide our urban livelihood and quality of life are highly complex and extremely fragile. City leaders strive to keep up with the growing population, limited resources, and expectation of society. Digital Twins, where the physical world is reflected and mirrored with technology with real time data, are already demonstrating how invaluable they are in providing resilient and sustainable solutions and helping to deliver the promise of smart cities. In their most basic form, digital twins of cities, i.e., in this case interactive models of cities, are already being used by city stakeholders for the understanding, communication and simulation in key areas such as urban planning, mobility and resource management. They drastically reduce the cost, not only during design, but provide a far greater likelihood of success and achieving community engagement and adoption. As technology evolves and open urban data becomes readily available and interoperable, the future of digital twins promises to deliver decision support systems. Platforms that will model not just the physical world but integrate the complexity that reflect the many facets and knock-on effects of our cities: from its differing social, cultural, political and multidisciplinary flavours. These decision support systems will provide local authorities with digital twins of their cities. The core tools to help them on both strategic and operational levels to: build evolving strategies; simulate and communicate what they wish to achieve to stakeholders and local communities involved; analyze the budgets and resources required to achieve success; monitor solutions based on KPIs; predict pain points and resolve before they become reality. The goal of digital twins for cities is to help bring about resilience and sustainability and stimulate smart growth to remain competitive and thrive.

The nuclear digital twin (DT) is the virtual representation of a nuclear energy system across its lifecycle. The nuclear DT uses real-time information and other data sources to improve the process of design, licensing, construction, security, O&M, decommissioning, and waste disposal. By leveraging the knowledge base and experience from the past 40 years of LWR operation, the nuclear DT is helping to accelerate the development and deployment of advanced nuclear technology in areas of passive safety, new fuel forms, instrumentation, and reactor control. For the currently operating nuclear fleet, DTs are reducing the operational risks, increasing plant availability, increasing energy capability, and reducing electricity production costs. For advanced fission and fusion reactors, DTs are being used to design for passive safety and built-in security-by-design. Rapidly deployable small modular reactor (SMR) and microreactor designs compatible with modular construction techniques and advanced manufacturing will be the new normal, reducing the need for large capital expenditures and compressing construction schedules. In addition, lower operational and maintenance costs will be realized by reducing the complexity of operations, staffing needs, and maintenance-related activities.

Health Digital Twins (HDT) can bring a decisive contribution to personalized, precise, successful medical treatments. They will rely on combining the latest fundamental knowledge from research with a patient’s exact history and unique physiology. First use cases of HDT have been developed in the last decade in specific domains, with the examples of Living Heart and Living Brain. They will extend to other domains, such as cell models, microbiota or patient cohorts, and support multi-discipline and multi-scale platforms. HDT will soon cover all medical disciplines and the whole patient journey, powering the next-generation medical practices, precision medicine and surgery, and allowing for improved patient autonomy – towards better health for all.

With the 5G era evolving continuously towards maturity, ICDT deep convergence is accelerating, and so is the transformation of our society. Simultaneously, advanced exploration of the 6G vision where a new era of “Digital Twins, Pervasive Connectivity with Ubiquitous Intelligence” is emerging. We expect the physical, biological, and cyber worlds to be fused together via the new generation of intelligent connectivity. In such an era, everyone may have his or her own Personal Digital Twin (PDT) in the cyber world. Such a PDT may include the person’s external appearance, as well its internal organs and tissues. The PDT may be used to predict human health, behavior, and emotion in advance of developments warranting concerns or requiring attention, and even simulate people’s thoughts to realize spiritual immortality in some extreme sense. Our PDTs can be transmitted to any place in the world in real time through the pervasive network for various activities, be it attending a concert, enjoying the beaches and sunshine of the Maldives, or hugging family members and friends that are oceans apart.This chapter will examine the usage scenarios of PDT, the challenges and the wide varieties of technologies involved, encompassing PDT information acquisition, transmission, processing, and presentation. In particular, wireless body area networks (WBANs) and multi-source data fusion will be highlighted. PDTs will rely on the joint development of many technologies from multiple disciplines, such as brain-computer communications, molecular communications, synesthesia interconnection, AI, and intelligent interaction. We will present a variety of health-care applications as part of a very early-stage list of accomplishments in our PDT platform development as well as the outlook of PDT related developments across the globe.

Until 30 years ago, Cultural Heritage studies entailed the use of books as well as the direct viewing of many works of art, from paintings to sculptures, from architectural masterpieces to museums, to entire cities. The material assets forming a country’s Cultural Heritage have a vital and irreplaceable intrinsic value, but due to their very physical nature, they are subject to damages, significant modifications and even loss.A country’s Cultural Heritage has the power to stimulate the emotions of those experiencing it and this is why it has always sparked the universal desire to pass it on by narrating it and reproducing it. Already in the first half of the third century BCE, Callimachus wrote “A Collection of Wonders around the World”, which unfortunately was lost. The first surviving literary work that included a list of world wonders, is an epigram by poet Antipater of Sidon included in the Palatine Anthology (9, 58), “I have gazed on the walls of impregnable Babylon along which chariots may race, and on the Zeus by the banks of the Alpheus, I have seen the hanging gardens, and the Colossus of the Hellos, the great man-made mountains of the lofty pyramids, and the gigantic tomb of Mausolus; but when I saw the sacred house of Artemis that towers the clouds, the others were placed in the shade, for the sun himself has never looked upon its equal outside Olympus.” Today, after well over 2000 years, we are still studying the Seven Wonders of the World.Through time, the desire to reproduce Cultural Heritage pieces was fulfilled in different ways: from drawings to casts, from photographs to postcards. Today this desire/need to create twin copies is done digitally, since computers have become the language of our time and are opening a multitude of scenarios and opportunities well beyond the mere reproduction of works of art.Step by step, and with some delays compared to other fields, the latest technologies are being applied to the Cultural Heritage world and today this world is finally acquiring digital twins: exact copies of physical objects and settings resulting from the Internet of Things. Thanks to the Internet of Things in fact, creating digital twins has become more accessible and financially attainable.What does the term digital twin mean in the world of art? The first thought that comes to mind is that a digital twin is just an exact copy of the physical work of art. And this is indeed true; however, it is a lot more, because it has opened innovative scenarios on multiple fronts. Thanks to digital twins, we can replicate lost pieces, enter museum halls, print 3D copies, monitor, and manage the security of works of art, and acquire a large volume of valuable data that can be used to conduct research and create multiple outputs, but digital twins have also become works of art themselves.During the pandemic, the support provided by digital twins has been extremely important, because it has allowed remote viewing when in person viewing had been suspended. The impact of Digital Twins is also expanding the selection offered within the Cultural Tourism sector, a key element for cultural heritage, which is experiencing an increasing involvement by users themselves.Faced with the huge volume of digital cultural heritage data available today, it appears clear that our real challenge is creating a common language based on global standards and inevitably, these standards will also have to include Artificial Intelligence.

Learning Factories (LFs) enable learning in a factory environment, and – due to the possibility of experiential learning – in manufacturing they are considered the most promising approach to acquire the skills necessary to succeed in the increasingly complex and technologically driven workplace, political, and social arenas of the twenty-first century. Due to the modelling capabilities at the basis of this technology, Digital Twin (DT) can support the implementation of LFs..In this chapter, the role of DT in manufacturing education is explored through two illustrative examples. Here, the DT technology is utilized to build digital LFs adopted for learning purposes. The first example shows a virtual flow shop that allows students to learn about: (i) Scheduling; (ii) Condition-based Maintenance; (iii) Internet of Things. Whereas in the second example, Virtual Commissioning (VC) is utilized to virtually verify the PLC (Programmable Logic Controller) code before its deployment, allowing students to learn both PLC programming and code verification techniques. The implemented teaching activities were targeted both to students from university and vocational schools. Furthermore, they dealt with different phases of the lifecycle of manufacturing processes. Throughout this chapter, it will be demonstrated that the application of the DT technology to LFs enables the building of a flexible teaching environment that can be customized based on the type of students and the competences that must be taught.

In spite of the increasing usage and development of Digital Twin solutions, the evolution is characterized by different niches with different needs and requirements. Some application domains are more advanced of others and they can be used to explore the possible “futures”. Different steps in the evolution are identified and presented. The next steps of evolution are identified in terms of major technological challenges to be solved (such as (Data Capture and Management, increased Intelligence, Visualization, Autonomy, Swarm Intelligence and so forth). This chapter sheds a light on the future by introducing some future application cases ranging from healthcare, automotive and construction up to the spatial web and the digital Twin of everything. The chapter concludes providing a perspective on open issues (from standards to federation to platform creation) and points to relevant technological enablers that may accelerate a wide adoption of the Digital Twin.

A myriad of laws, rules, and regulations are worthy of consideration for any new and innovative technology, and even more so for one as broad ranging and comprehensive as the Digital Twin ecosystem. A technology like this has the contradiction of open versus proprietary, and all the hybrids in between, because it is in the early stages of its evolution that, in many respects, relies on a combination of existing technologies and innovations. From a legal standpoint, we consider intellectual property rights, including patent, copyright, and trade secret protection, and balancing those rights with the benefits and protections available under contract law. The wide applicability of the Digital Twin to various technologies and fields, such as healthcare, finance, education, aviation, power plants, nuclear reactors, any many more, gives rise to regulatory considerations and ethical concerns. The Digital Twin ecosystem, as applied in these areas and more, requires the collection, processing, generation, and transmission of data subject to regulatory requirements involving privacy and cybersecurity issues, as well as ethical concerns requiring careful consideration of potential bias, trustworthiness, and transparency in the technology used.

The Digital Twin is crucial and timely for positively affecting how we work, live, and play. It eliminates the gap between experimentation and learning by bridging real and virtual worlds in a powerful methodology, making significant headway in conquering previously unsolvable problems and challenges. Digital Twins are made possible by four widely deployed infrastructures for connectivity and communications, computing, digital storage, and sources of digital data. The Digital Twin provides insights, paths to innovation, efficient production of goods, improved delivery of services, better experiences and entertainment, and new business models. Investing in Digital Twins is one of the most valuable ways to create sustainable paths to the future.While Digital Twins bring concrete value today for a wide assortment of applications in different vertical domains today they are still in the early stages of evolution. The considerations in the future of Digital Twins are the business cases and models that accompany Digital Twins, the mind set of the management, engineers and technologists, the development for ecosystems in individual industries, and finally the trust of end-use beneficiaries. Digital Twins can be simple, but more than likely, where they bring transformative value they will be complex technologically and will require significant changes in organizational structures to be successful. From what we have learned it is consequently important to understand that the commitment of an enterprise or a business to adopt Digital Twins as a methodology is a journey. It includes incentives for those involved, the development of human capabilities, significant acquisition of tools and infrastructure, the maturation of processes across the enterprise or business, and integration with a supporting ecosystem.The Digital Twin book is the most comprehensive work on the subject to date. It has brought together top practitioners, technical experts, analysts, and academics to explore and discuss the concept of the Digital Twin, its history, evolution, and the profound impact across sectors of the global economy. The book addresses the business value, technological underpinnings, lessons learned from implementations, resources for success, practical approaches for implementation, and illustrative use cases. It makes the case for why we believe that Digital Twins will fundamentally transform major industries and enable us to fulfill important societal goals.