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Technology Trends/Digital Twins (view source)

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A Digital Twin will often make use of technology such as artificial intelligence, machine learning, and software analytics. The data is acquired from multiple sources including humans, similar machines, large systems, as well as the environment it resides in. Digital Twins have the potential to completely change certain industries. For example, when designing an integrated solution architecture a Digital Twin can integrate data from cloud platforms as well as asset related applications. This creates a single source of data and analysis for every asset.

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Since real-time data provided through IoT sensors is integrated by a Digital Twin, a variety of use cases exist for their application. An organization can apply the technology to their sell-able products turning them into connected products where they are able to perform Product-Life-Cycle-Management from the design phase to the service provided to the customer [1]. Manufacturers can also benefit by connecting their end to end processes within a production Digital Twins can provide the capability to offer new product-as-a-service business models. Doing so also allows a Digital Twin to transition from being a data-driven simulation model to a tool for financial accounting and planning. In the context of Enterprise Architecture an architect can create an EA blueprint as a Digital Twin for the organization.

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Since real-time data provided through IoT sensors is integrated by a Digital Twin, a variety of use cases exist for their application. An organization can apply the technology to their sell-able products turning them into connected products where they are able to perform Product-Life-Cycle-Management from the design phase to the service provided to the customer <ref>Impact of the digital twin on the enterprise architecture. (n.d.). Retrieved from [ https://blogs.sap.com/2018/09/04/impact-of-the-digital-twin-on-the-enterprise-architecture/]</ref>. Manufacturers can also benefit by connecting their end to end processes within a production Digital Twins can provide the capability to offer new product-as-a-service business models. Doing so also allows a Digital Twin to transition from being a data-driven simulation model to a tool for financial accounting and planning. In the context of Enterprise Architecture an architect can create an EA blueprint as a Digital Twin for the organization.

<h2>Technology Brief</h2>

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A Digital Twin can utilize a combination of Artificial Intelligence (AI) and Machine Learning (ML) to correctly represent current and future states of a physical asset Digital Twin. Developing a Digital Twin requires an information communication technology framework integrated with physical properties as well as software for data visualization. This means the twin requires the proper processing power for the data where trends and analysis can be represented or visualized on a dashboard. This can represent real world events as well as the characteristics of objects and processes [9]. A Digital Twin can be thought of as a software module or a series of data sets that is logically distinct from an application using the twin. The application can then interact with the twin rather than the actual object. Gartner proclaims that the top practice for applications interacting with a twin is through a well-defined interface [4]. This can be an event-based API or a request reply API. When public API methods or functions are invoked the twin’s logic can then perform a variety of actions including receiving data or generating alerts, or whatever functionality has been implemented based on system requirements. By using an API the twin’s data and logic is encapsulated and decoupled from the application’s logic, making the system loosely coupled. The API is exposed on the twin’s side, allowing the application to make calls to the lower level software modules that process the twin’s data. This now means as long as the semantics of interface are maintained, either the twin or the application can be altered without harmful changes propagating through the entire system [4]. This also means that a twin can be shared and accessed by multiple applications. There are several advantages to this including the avoidance of data duplication, a common operating picture of the state of an object, a reduction in the number of communication protocol stacks and network ports needed, and lastly security can be improved because all network traffic through the object can be redirected through the twin providing a single point of entry.

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A Digital Twin can utilize a combination of Artificial Intelligence (AI) and Machine Learning (ML) to correctly represent current and future states of a physical asset Digital Twin. Developing a Digital Twin requires an information communication technology framework integrated with physical properties as well as software for data visualization. This means the twin requires the proper processing power for the data where trends and analysis can be represented or visualized on a dashboard. This can represent real world events as well as the characteristics of objects and processes <ref> https://blogs.sap.com/2018/09/04/impact-of-the-digital-twin-on-the-enterprise-architecture/[https://www.designnews.com/electronics-test/steps-creating-digital-twin/34989275659708]</ref>. A Digital Twin can be thought of as a software module or a series of data sets that is logically distinct from an application using the twin. The application can then interact with the twin rather than the actual object. Gartner proclaims that the top practice for applications interacting with a twin is through a well-defined interface <ref>Gartner_Inc. (n.d.). Why and How to Design Digital Twins. Retrieved from [https://www.gartner.com/en/documents/3888980/why-and-how-to-design-digital-twins]</ref>. This can be an event-based API or a request reply API. When public API methods or functions are invoked the twin’s logic can then perform a variety of actions including receiving data or generating alerts, or whatever functionality has been implemented based on system requirements. By using an API the twin’s data and logic is encapsulated and decoupled from the application’s logic, making the system loosely coupled. The API is exposed on the twin’s side, allowing the application to make calls to the lower level software modules that process the twin’s data. This now means as long as the semantics of interface are maintained, either the twin or the application can be altered without harmful changes propagating through the entire system <ref>Gartner_Inc. (n.d.). Why and How to Design Digital Twins. Retrieved from [https://www.gartner.com/en/documents/3888980/why-and-how-to-design-digital-twins]</ref>. This also means that a twin can be shared and accessed by multiple applications. There are several advantages to this including the avoidance of data duplication, a common operating picture of the state of an object, a reduction in the number of communication protocol stacks and network ports needed, and lastly security can be improved because all network traffic through the object can be redirected through the twin providing a single point of entry.

<h2>Industry Usage</h2>

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Several organizations have remarked the benefits of Digital Twins, and are now using them to monitor their physical assets, operation dynamics, and business processes. General electric is one of the largest firms currently making use of Digital Twins. The company had begun using the twins to analyse data collected from the wind turbines, oil rigs and air craft engines they produce. For example when using the twin on an aircraft engine the system will monitor the engine and all its sub-components during a flight. A real-time Digital Twin is then generated from the data transferred from the on-board sensors to the company’s datacenter [3]. If a potential defect is detected they are able to determine precisely which part is causing the fault and have a replacement ready once the aircraft has landed. The technology also has huge medical potential. A research collaboration between Stanford University and HPE called “The Living Heart” creates a 3D multiscale model of a heart [11]. From there circulation can monitored and medications are able to be virtually tested. Much like its use in aircraft engine maintenance automotive vendors are also noting the potential of Digital Twins. Volkswagen has begun using the technology to monitor their production process. They have also begun using the Digital Twin in combination with Augmented Reality via Microsoft HoloLens [10]. Engineers and designers are now able to modify the Digital Twin using gesture control and vocal commands.

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Several organizations have remarked the benefits of Digital Twins, and are now using them to monitor their physical assets, operation dynamics, and business processes. General electric is one of the largest firms currently making use of Digital Twins. The company had begun using the twins to analyse data collected from the wind turbines, oil rigs and air craft engines they produce. For example when using the twin on an aircraft engine the system will monitor the engine and all its sub-components during a flight. A real-time Digital Twin is then generated from the data transferred from the on-board sensors to the company’s datacenter <ref>Digital Twin. (n.d.). Retrieved from [https://www.ge.com/digital/applications/digital-twin]</ref>. If a potential defect is detected they are able to determine precisely which part is causing the fault and have a replacement ready once the aircraft has landed. The technology also has huge medical potential. A research collaboration between Stanford University and HPE called “The Living Heart” creates a 3D multiscale model of a heart <ref>Goh, D. E. (2018, July 09). How Digital Twins of the Human Body Can Advance Healthcare. Retrieved from[https://www.hpe.com/us/en/newsroom/blog-post/2018/07/how-digital-twins-of-the-human-body-can-advance-healthcare.html]</ref>. From there circulation can monitored and medications are able to be virtually tested. Much like its use in aircraft engine maintenance automotive vendors are also noting the potential of Digital Twins. Volkswagen has begun using the technology to monitor their production process. They have also begun using the Digital Twin in combination with Augmented Reality via Microsoft HoloLens <ref>Volkswagen Inside. Retrieved 8 October 2018.[http://inside.volkswagen.com/The-virtual-twin.html]</ref>. Engineers and designers are now able to modify the Digital Twin using gesture control and vocal commands.

<h2>Canadian Government Use</h2>

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Since Digital Twin is a model of the actual physical object, it can be easily interacted with by analysts to track asset status, simulate unique conditions, and perform what-if analysis to predict failures. The main purpose of a Digital Twin is to act as a proxy for its thing, so any application that needs data from the physical object deals directly with the proxy. Since Digital Twin is a piece of software, it can be programmed to encapsulate data so that changes can be made within the twin without affecting any connected applications, and vice versa. Additionally, as a model, Digital Twin helps analysts understand, document, and explain the behavior of a specific machine or a collection of machines over a specified amount of time, improving asset management techniques.

Digital Twins can further increase an organization’s situational awareness by analysis of sensor IoT data and information. The rise of Digital Twin technology coincides with the rise of the IoT and AI/ML. Future advances and investments in both IoT and AI/ML are expected and this continues to support the development of Digital Twin technology. Digital Twin technology is becoming increasingly beneficial because it possesses capabilities that decrease the complexity of IoT ecosystems by creating easy to work with digital models of a physical object. Although, Digital Twins vary greatly in their purposes and the amount of data they hold, they all follow the same principle, there is one twin per physical thing. This decreases complexity for network analysts and improves their situational awareness of the network by identifying crucial physical assets which require organizational monitoring and management.

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Digital Twin can also be leveraged to drive business process management. A contextualized model can be created by a Digital Twin for individual business processes or work processes. This allows an organization to identify parts of an organization that are directly providing enterprise value. For example, enterprise risk management is a complex process often involving multiple stakeholders, within an organization, a Digital Twin can be leveraged to create visibility on the dependencies within various aspects of this process. A Digital Twin can provide accountability and governance as well as performance indicators and objectives. Making the entire process more visible and easily trackable [6]. When combining Digital Twin data with business rules, optimization algorithms or other prescriptive analytics technologies, Digital Twins can support human decisions or even automate decision making.

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Digital Twin can also be leveraged to drive business process management. A contextualized model can be created by a Digital Twin for individual business processes or work processes. This allows an organization to identify parts of an organization that are directly providing enterprise value. For example, enterprise risk management is a complex process often involving multiple stakeholders, within an organization, a Digital Twin can be leveraged to create visibility on the dependencies within various aspects of this process. A Digital Twin can provide accountability and governance as well as performance indicators and objectives. Making the entire process more visible and easily trackable <ref>Gartner_Inc. (n.d.). 12 Powerful Use Cases for Creating a Digital Twin of Your Organization. Retrieved from [https://www.gartner.com/doc/3817018/-powerful-use-cases-creating]</ref>. When combining Digital Twin data with business rules, optimization algorithms or other prescriptive analytics technologies, Digital Twins can support human decisions or even automate decision making.

Lastly, improved situational awareness and asset management provided by Digital Twins can be used to help make better strategic business decisions. There are three types of Digital Twin with varying values to enterprise decision making, they are: Discrete, Composite and Digital Twin Organization, also known as Product, Production, and Performance. Discrete/Product emphasizes monitoring physical objects (individual assets), Composite/Production emphasizes operations involving a combination of discrete/product Digital Twins and resources (things, people, and processes), and Digital Twins Organization/Performance emphasizes on monitoring processes across entire business operations (maximizing business processes).

<h4>Challenges</h4>

There are many challenges that SSC could face in the development and deployment of Digital Twins in coordination with IoT devices. Most notable is the large amount of time, guidance, effort, resources, and funding required for establishing and maintaining Digital Twins and a robust GC IoT program that also has a high level of interoperability. Additional planning will be needed for SSC’s infrastructure to accommodate increased Digital Twin requirements.

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One of the biggest challenges with regards to Digital Twins is the overwhelming convergence of IoT data from sources such as Digital Twins, design, and process and quality control data, with existing data from legacy systems. Often it is the system supporting the Digital Twin which becomes overloaded. This is because the goal of Digital Twin adoption is often to provide a full lifecycle view of a product in service. The processing of this data is a concern since many Digital Twin solutions involve the use of AI or ML to provide real-time representations of the physical assets. IoT sensors will need to make use of edge computing and processing since handling all of the raw data entirely in-house can become overwhelming when the IT infrastructure cannot support it [8]. However, even when edge computing shares the processing burden, a data center may still be overloaded with Digital Twin data processing demands.

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One of the biggest challenges with regards to Digital Twins is the overwhelming convergence of IoT data from sources such as Digital Twins, design, and process and quality control data, with existing data from legacy systems. Often it is the system supporting the Digital Twin which becomes overloaded. This is because the goal of Digital Twin adoption is often to provide a full lifecycle view of a product in service. The processing of this data is a concern since many Digital Twin solutions involve the use of AI or ML to provide real-time representations of the physical assets. IoT sensors will need to make use of edge computing and processing since handling all of the raw data entirely in-house can become overwhelming when the IT infrastructure cannot support it <ref>HarperDB. (n.d.). Insights and Updates. Retrieved from [https://www.harperdb.io/blog/all]</ref>. However, even when edge computing shares the processing burden, a data center may still be overloaded with Digital Twin data processing demands.

Accompanying data processing issues is the challenge of interoperability. If crucial physical assets are being monitored using Digital Twin technology this requires a high level of availability and interoperability between the Digital Twin technology, the IoT, and the hosting infrastructure. This is very challenging given that the GC network is large, highly disparate, and has numerous legacy systems that were never designed to interoperate with each other. Integrating IoT-connected products can be a complicated task, and since Digital Twin is an intrinsic part of IoT the implementation of interoperable IoT is an intractable issue when discussing Digital Twin. In order to successfully implement a Digital Twin project it would require a significant number of sensors. In a lot of cases this can be cost prohibitive. Additionally, managing the deployment of so many sensors is complex and time consuming. Hardware can also become a bottleneck within the IoT space since many vendors of sensors will require early prototypes of physical assets they are designing sensors for, to verify their design. A corporation making use of the IoT sensors to create their Digital Twin will be forced to purchase a significant number of sensors which can be expensive when the complexity of these sensors is high.

Governance is also a major challenge as over 85% of Digital Twins are managed by multiple-stakeholders. This brings this issue of ownership and visualization access needs to the forefront of managing Digital Twins. There is the issue of who actually owns the Digital Twin and the data populating it.

Connectivity is another major challenge for many Digital Twin concepts. Most physical objects that are both vital and interesting to study from a Digital Twin perspective, do not remain stationary. Making sure a connection or network reception can be established all the time to a network is a challenge moving forward when the number of physical assets being tracked on an organizations network is large and in semi-continual motion. Most IoT architectural patterns currently rely on data caching on the edge and processing in the cloud models. However, the bandwidth required to gain value from a Digital Twin scenario that could potentially be processing billions of data points is a tremendous connectivity issue.

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Another challenge in pursuing Digital Twins is the shifting of an enterprise’s business model. The enterprise must now place Digital Twins as a core component of their business model and this can be difficult with large organizations who are more susceptible to changes in their business model [7]. The Digital Twin can then be used to support strategy execution. However, leveraging a Digital Twin in this manner can only be done when it is provided with full visibility into business processes and performance and any interdependencies between them [6].

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Another challenge in pursuing Digital Twins is the shifting of an enterprise’s business model. The enterprise must now place Digital Twins as a core component of their business model and this can be difficult with large organizations who are more susceptible to changes in their business model [7]. The Digital Twin can then be used to support strategy execution. However, leveraging a Digital Twin in this manner can only be done when it is provided with full visibility into business processes and performance and any interdependencies between them <ref>Gartner_Inc. (n.d.). 12 Powerful Use Cases for Creating a Digital Twin of Your Organization. Retrieved from [https://www.gartner.com/doc/3817018/-powerful-use-cases-creating]</ref>.

<h4>Considerations</h4>

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