Digital Twin – Digital Twin: 3D visualization and digital supply chain management

Digital Thread is defined as “the use of digital tools and representations for design, evaluation and life cycle management”.

The term “Digital Thread” was first used in the USAF Global Science and Technology Vision Task Force report “Global Horizons 2013”.

The term Digital Thread was further refined by Singh and Willcox at MIT in their 2018 paper entitled "Engineering with a Digital Thread." In this academic paper, the term Digital Thread is defined as "a data-driven architecture that links information from across the entire product lifecycle and is intended to serve as the primary or authoritative data and communication platform for an organization's products at any given time."

In a narrower sense, the term "digital thread" is also used to refer to the lowest design and specification level for a digital representation of a physical object. The digital thread is a crucial capability in model-based systems engineering (MBSE) and the foundation for a digital twin.

The term Digital Thread is also used to describe the traceability of the digital twin to the requirements, parts, and control systems that comprise the physical object.

Smart Factory – Using business-relevant concepts in Germany – Image: Xpert.Digital

The graphic shows the results of a 2017 survey of managing directors of German industrial companies regarding the technologies used in smart factories today and in the future. 23 percent of respondents stated that they currently use the digital twin of their products in their smart factory. 43 percent indicated that they plan to use the digital twin of their products in the future.

This also applies to autonomous internal logistics: 17% stated that they currently (2017) use it. 35% plan to implement it by 2022.

Usage in five years (2022)

• Data-driven resource optimization – 77%
• Integrated planning – 61%
• Big data-driven process and quality optimization – 65%
• Modular production systems / modular production assets – 36%
• Networked factory / Connected factory – 60%
• Predictive maintenance – 66%
• Process visualization/automation – 62%
• Digital twin of the product / Digital twin of the product – 43%
• Digital twin of the factory / Digital twin of the factory – 44%
• Digital twin of the production plant / Digital twin of the production asset – 39%
• Flexible production methods / Flexible production methods – 34%
• Autonomous intra-plant logistics / Autonomous intra-plant logistics – 35%
• Transfer of production parameters – 32%
• Fully autonomous digital factory – 11%

Usage today (2017)

• Data-driven resource optimization – 52%
• Integrated planning – 32%
• Big data-driven process and quality optimization – 30%
• Modular production systems / modular production assets – 29%
• Networked factory / Connected factory – 29%
• Predictive maintenance – 28%
• Process visualization/automation – 28%
• Digital twin of the product / Digital twin of the product – 23%
• Digital twin of the factory / Digital twin of the factory – 19%
• Digital twin of the production plant / Digital twin of the production asset – 18%
• Flexible production methods / Flexible production methods – 18%
• Autonomous intra-plant logistics / Autonomous intra-plant logistics – 17%
• Transfer of production parameters – 16%
• Fully autonomous digital factory – 5%

Managing directors of German industrial companies were surveyed. The question was phrased as follows: “How relevant are the following concepts for your company?” The source provides no information on the survey methodology or on scores exceeding 100 percent.

One example of how digital twins are used to optimize machines is the maintenance of energy generation plants such as turbines, jet engines and locomotives.

Another example of digital twins is the use of 3D models to create digital companions for physical objects. This allows the status of the actual physical object to be displayed, providing a way to project physical objects into the digital world. For example, if sensors collect data from a connected device, the sensor data can be used to update a copy of the device's state as a "digital twin" in real time. The term "device shadow" is also used for the concept of the digital twin. The digital twin is intended to be a current and accurate copy of the properties and states of the physical object, including shape, position, gestures, status, and movement.

A digital twin can also be used for monitoring, diagnostics, and forecasting to optimize the performance and utilization of assets. In this area, sensor data can be combined with historical data, human expertise, and fleet and simulation learning to improve forecasting results. Therefore, complex forecasting and intelligent maintenance platforms can leverage digital twins to identify the root cause of problems and improve productivity.

Digital twins of autonomous vehicles and their sensors, embedded in a traffic and environmental simulation, have also been proposed as a means of overcoming the significant challenges in the development, testing and validation of applications in the automotive industry, especially when the relevant algorithms are based on artificial intelligence approaches that require extensive training and validation datasets.

The physical manufactured objects are virtualized and represented as digital twin models (avatars) that are seamlessly and tightly integrated in both physical and cyberspace. Physical objects and twin models interact in a mutually beneficial way.

Dynamics at the industry level

The digital twin is transforming the entire product lifecycle management (PLM) process, from design and manufacturing to service and operation. Currently, PLM is very time-consuming in terms of efficiency, manufacturing, intelligence, service phases, and sustainability in product design. A digital twin can merge the physical and virtual spaces of a product. It allows companies to create a digital footprint of all their products, from design and development throughout their entire lifecycle. Generally, industries involved in manufacturing are significantly impacted by digital twins. In the manufacturing process, the digital twin is a virtual replica of real-time operations on the factory floor. Thousands of sensors are placed throughout the physical manufacturing process, collecting data from various dimensions, such as environmental conditions, machine behavior, and work performed. All this data is continuously transmitted and collected by the digital twin. Thanks to the Internet of Things (IoT), digital twins have become more affordable and could shape the future of the manufacturing industry. One advantage for engineers is the real-world use of products designed virtually using the digital twin. Advanced methods of product and plant maintenance and management are becoming more accessible, as a digital twin of the real “thing” with real-time capabilities is available.

Digital twins offer significant business potential because they predict the future rather than analyzing the past of the manufacturing process . The representation of reality created by digital twins enables manufacturers to evolve toward ex-ante business practices. The future of manufacturing is based on the following six aspects:

• Scalability,
• Modularity,
• flexibility
• Autonomy,
• Connectivity
• and digital twin.

With the increasing digitalization of individual phases of a manufacturing process, opportunities arise to achieve higher productivity. This begins with modularity and leads to greater efficiency in the production system. Furthermore, autonomy enables the production system to react efficiently and intelligently to unexpected events. Finally, connectivity, such as the Internet of Things, closes the digitalization cycle by allowing the subsequent product design and marketing cycle to be optimized for higher performance. This can lead to greater customer satisfaction and loyalty if products can detect a problem before it actually fails. As the costs of storage and data processing continue to decrease, the potential applications of digital twins are also expanding.

The digital twin holds particular significance for industry. Its existence and use in industrial value creation processes can provide companies with a decisive competitive advantage. This has been especially true since the early 2010s, when the Internet of Things (IoT) enabled the production of digitally controlled and networked products of all kinds, along with integrated services.

In industry, digital twins exist for products, production facilities, processes, and services, for example. They can even exist before the physical twin, such as design models of future products. And they can be used to analyze and evaluate data from the use of the physical twins. They serve a wide variety of purposes and functions.

Their particular value for industry stems from the elimination of physical prototypes and the ability to simulate the behavior, functionality, and quality of the real-world twin in every relevant aspect. This value can be leveraged for all parts of the value chain throughout the entire life cycle of products, systems, and services.

A digital twin takes many different forms. For example, it can be based on a behavioral model of the system development, a 3D model, or a functional model that realistically and comprehensively depicts the mechanical, electronic, and other properties and performance characteristics of the real twin during a model-based design process.

The various digital twins can be linked together and also allow extensive communication and interaction with their physical counterparts. This is also referred to as a digital thread that runs through the entire product lifecycle and can include further product-relevant information. A company derives the greatest benefit from such a continuous digital thread, which enables optimization across various value creation processes and the exploitation of a wide range of digital business models for products or services offered.

Production engineering is just one of many industrial applications. Digital twins map systems throughout their entire lifecycle (design, construction, operation, and recycling). Even during the planning phase, engineers can use simulation models to optimize processes. Once the system is operational, the same simulation models can be used to further optimize processes and transform production.

In the transport and warehousing sectors, international logistics companies like DHL and UPS are continuously developing new applications for digital twins, such as track and trace or the intelligent control of warehouses and entire port facilities. Software manufacturers like SAP and Oracle are expanding their ERP systems and offering new IT solutions as digital supply chains for supply chain management.

The concept of the digital twin is increasingly being applied in production control, logistics, and procurement. This allows the concept to be closely linked to the methods and tools of control engineering and automation technology.

Geographic digital twins have become popular in urban planning practice due to the increasing interest in digital technology within the smart cities movement. These digital twins are often proposed in the form of interactive platforms for capturing and visualizing 3D and 4D spatial data in real time to model urban environments (cities) and the data they contain.

Visualization technologies such as augmented reality (AR) systems are used both as collaborative tools for design and planning in the built environment and for integrating data feeds from embedded sensors in cities and API services to create digital twins. For example, AR allows augmented reality maps, buildings, and data to be projected onto tabletops for collaborative viewing by construction professionals.

In the construction industry, planning, design, construction, operation, and maintenance activities are becoming increasingly digitalized – partly through the introduction of BIM (Building Information Modeling) processes – and digital twins of buildings are seen as a logical extension – both at the level of individual buildings and at the national level. In the United Kingdom, for example, the Centre for Digital Built Britain published the Gemini Principles in November 2018, which outline the principles for developing a “national digital twin”.

One of the earliest examples of a working “digital twin” was implemented in 1996 during the construction of the Heathrow Express facilities at Heathrow Airport's Terminal 1. Consultant Mott MacDonald and BIM pioneer Jonathan Ingram connected motion sensors in the cofferdam and boreholes to the digital object model to display movement within the model. A digital injection object was created to monitor the effects of pumping grout into the ground to stabilize soil movements.

Healthcare is considered an industry being transformed by digital twin technology. The concept of digital twins in healthcare was originally proposed and first implemented for product or device predictive analytics. With a digital twin, lives in medicine, sports, and education can be improved by adopting a more data-driven approach to healthcare. The availability of technology makes it possible to create personalized patient models that can be continuously updated based on collected health and lifestyle parameters. This can ultimately lead to a virtual patient that describes an individual's health status in detail, rather than relying solely on past records. Furthermore, the digital twin allows for comparing an individual's records with those of the population to more easily identify patterns with a high degree of accuracy. The greatest benefit of digital twins for healthcare is the ability to tailor healthcare to individual patient responses. Digital twins will not only lead to more precise definitions of an individual patient's health but will also change the perceived image of a healthy patient. Previously, "healthy" was defined as the absence of any signs of illness. Now, “healthy” patients can be compared with the rest of the population to define true health . However, the advent of digital twins in healthcare also brings some drawbacks. Digital twins can lead to inequality, as the technology may not be accessible to everyone and could widen the gap between rich and poor. Furthermore, digital twins will detect patterns within a population that could lead to discrimination.

The concept of the digital twin is also gaining traction in medicine, where a virtual representation of a patient is created to simulate medical procedures. This allows doctors to familiarize themselves with the patient's specific situation before treatment, and in surgical operations, patient-specific implants (e.g., artificial joints) can be prefabricated and precisely inserted, leading to improved surgical outcomes and faster recovery.

The automotive industry has been enhanced by digital twin technology. Digital twins in the automotive industry are implemented by leveraging existing data to simplify processes and reduce marginal costs. Currently, automotive engineers are augmenting existing physical materiality by incorporating software-based digital capabilities. A concrete example of digital twin technology in the automotive industry is that automotive engineers use digital twin technology in combination with the company's analytics tools to analyze how a particular car is driven. This allows them to propose new features for the car that can reduce the number of accidents on the road, something that was previously impossible to achieve in such a short time.

Digital technologies possess certain characteristics that distinguish them from other technologies. These characteristics, in turn, have specific consequences. Digital twins exhibit the following characteristics.

Connectivity

One of the key features of digital twin technology is its connectivity. The recent development of the Internet of Things (IoT) is giving rise to numerous new technologies. The development of the IoT is also driving the development of digital twin technology. This technology shares many characteristics with the nature of the IoT, namely its connectivity. Primarily, the technology enables connectivity between the physical component and its digital counterpart. This connection forms the basis of the digital twin, without which digital twin technology would not exist. As described in the previous section, this connectivity is established through sensors on the physical product that collect data and integrate and communicate this data via various integration technologies. Digital twin technology enables enhanced connectivity between companies, products, and customers. For example, connectivity between partners in a supply chain can be increased by enabling these partners to check the digital twin of a product or asset. These partners can then verify the status of that product simply by accessing the digital twin.

Connectivity with customers can also be increased.

Servitization refers to the process by which companies add value to their core offering through services. In the case of engines, the manufacturing of the engine is the core offering of this organization, which then provides added value by offering a service for engine inspection and maintenance.

Servitization

Servitization is a business model innovation relevant to manufacturing companies, referring to the shift in their existing product portfolio away from solely tangible goods and towards a combination of goods and services. It thus reflects the overall economic trend towards a service-based society at the company level.

Examples of servitization have existed for over 100 years. However, the topic has rapidly gained importance in the last 20 years or so because, due to globalization, companies in high-wage countries like Germany see it as a way to protect themselves against competition from low-wage countries. In academia, servitization has established itself as an independent research topic thanks to a research article by Sandra Vandermerwe and Juan Rada.

Homogenization

Digital twins can be characterized as a digital technology that is both a consequence and an enabler of data homogenization. Since any type of information or content can now be stored and transmitted in the same digital form, a virtual representation of the product (in the form of a digital twin) can be created, thereby decoupling the information from its physical form. The homogenization of data and the decoupling of information from its physical artifact have thus enabled the emergence of digital twins. Digital twins also make it possible to digitally store increasing amounts of information about physical products and decouple it from the product itself.

As data becomes increasingly digitized, it can be transferred, stored, and processed quickly and cost-effectively. According to Moore's Law, computing power will continue to increase exponentially in the coming years, while the cost of data processing will decrease significantly. This would therefore lead to lower marginal costs for developing digital twins and make it comparatively much cheaper to test, predict, and solve problems using virtual representations, rather than testing them on physical models and waiting for physical products to break down before taking action.

Another consequence of the homogenization and decoupling of information is the convergence of user experience. As information from physical objects is digitized, a single artifact can offer a multitude of new possibilities. Digital twin technology allows detailed information about a physical object to be shared with a larger number of agents, regardless of location or time. In his white paper on digital twin technology in the manufacturing industry, Michael Grieves notes the following regarding the consequences of the homogenization enabled by digital twins:

In the past, factory managers had their offices overlooking the factory, allowing them to get a sense of what was happening on the shop floor. With the digital twin, not only the factory manager, but everyone involved in factory production can have the same virtual window not just to a single factory, but to all factories worldwide.

Reprogrammable and intelligent

As mentioned earlier, a digital twin allows a physical product to be reprogrammed in a specific way. Furthermore, the digital twin can also be reprogrammed automatically using sensors on the physical product, artificial intelligence technologies, and predictive analytics. One consequence of this reprogrammability is the emergence of new functionalities. Taking the example of an engine again, digital twins can be used to collect data on the engine's performance and, if necessary, to adjust the engine and create a newer version of the product. Servitation can also be seen as a consequence of reprogrammability. Manufacturers can be responsible for monitoring the digital twin, making adjustments, or reprogramming it as needed, and they can offer this as an additional service.

Digital Traces

Another characteristic is the fact that digital twin technologies leave digital traces. These traces can be used by engineers to, for example, check the digital twin's history in the event of a machine malfunction, in order to diagnose where the problem originated. In the future, these diagnoses can also be used by the manufacturers of these machines to improve their designs, thus reducing the frequency of the same malfunctions.

Modularity

In the context of the manufacturing industry, modularity can be described as the design and adaptation of products and production modules. By adding modularity to manufacturing models, manufacturers gain the ability to optimize models and machines. Digital twin technology enables manufacturers to track the machines in use and identify potential areas for improvement. With modular machines, manufacturers can use digital twin technology to identify which components are impacting machine performance and replace them with better-suited components to improve the manufacturing process.