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Digital Twins

Expecting Digital Twins

When we design machines for a connected world, the traditional operation manager or engineer’s toolbox may look rather empty. As our assets and systems become more complicated, the way in which we develop for, manage and maintain them needs to evolve, too. We need tools to meet the new realities of software-driven products fuelled by digital disruption. Enter the digital twin. Digital twins are a design pattern for a new class of enterprise software component that produces a digital proxy for a thing, person or process. Digital twins are used to increase situation awareness, to better understand and respond to a business resource’s changing state, and to apply these capabilities more broadly to drive improvements in commercial processes and other forms of business value.

Through this whitepaper, we will categorize and define the emerging role for the three emerging types of digital twin, as well as their relationship to and impact on each other and business applications.

Analysis

Understanding Digital Twin Types and Relationships

Any digital twin can ingest and analyse data from all resources including things, people and processes. The difference is more in their emphasis and primary role:

  • Discrete digital twins are typically used to monitor and optimize the use of atomic resources such as people, individual products or pieces of equipment, and single process tasks.
  • Composite digital twins are typically designed to monitor and optimize the use of a related combination of discrete digital twins and atomic resources.
  • DTOs are typically designed for the purpose of monitoring and optimizing higher-order, business-level outcomes.

These distinctions, while defining big differences in relative degrees of scope, do not represent mutually exclusive roles for digital twins.

Digital twin design patterns and their key characteristics

Basic design pattern

In practice, all digital twins follow the basic digital twin design pattern as a shared, digital proxy for IoT-connected and other business resources, which, among other things, optimizes communications and data sharing. 

Proliferation 

While there are no absolute constraints, there can be wide variations depending on use case, industry and company size. In general, companies should plan for many discrete digital twins, fewer composite digital twins and even fewer DTOs.

Complexity

Any type of digital twin can range from being simple to complex (or from low to high fidelity).For example, a simple industrial pump digital twin may only monitor and control whether the pump is on or off. If more complex, it may also monitor and control the flowrate and report the amount of flow, the temperature and other factors.

Inheritance

The level and nature of digital twin inheritance can vary widely, depending on project needs. Digital twin inheritance can range from simple data sharing (a form of data integration) to programmatic inheritance — where one software object is “contained in” another.

Organization

Inherited digital twins are organized into directed graphs, with parent digital twins inheriting child digital twins, ad infinitum, as needed. In some cases, inheritance relationships can be hierarchical (for example, the manufacturing bill of materials’ parent-child relationship between finished products, their subassemblies and individual components).

Interoperability

Digital twin interoperability, in support of inheritance, can vary widely across IT projects — and even for different digital twin pairs in the same IT project. Options range from tightly coupled interoperability, including software object metadata inheritance (at one extreme), to loosely coupled interoperability via simple data integration (at the other extreme).

Recommendations

  • Design all digital twins to follow the basic digital twin design pattern as a shared, digital proxy for IoT-connected and other business resources.
  • Utilize discrete digital twins for atomic resources (people, products or equipment, process tasks), particularly IoT-connected resources.
  • Utilize composite digital twins for combinations of discrete digital twins and atomic resources, particularly to optimize the performance of processes across multiple resources.
  • Utilize DTOs to monitor and drive improvements and adapt to changing conditions in overall business outcomes across complex systems, particularly to drive outcomes such as agility, profitability, customer satisfaction or stakeholder value.
  • Adopt a flexible, pragmatic approach to digital twin inheritance and interoperability, supporting inheritance only when the complexity is well-understood, and cost justified.
  • Assess supporting industry groups (such as the Digital Twin Interoperability Task Group of the Industrial Internet Consortium) that are in the early stages of considering standards and frameworks to support digital twin interoperability.
  • Begin planning to deploy and manage a proliferation of digital twins that is likely to eventually involve dozens of, and in many cases hundreds or more, kinds of digital twins.

Understanding Digital Twins Enhancements Implementation and Integration

An important characteristic common to all three types of digital twin is that they perform two vital roles for business:

  • First, digital twins are used to improve situation awareness.
  • Second, the improved situation awareness provided by digital twins can be used to help make better business decisions.

When digital twins are not bundled with business applications there are several ways to acquire them:

  • Acquire digital twins as a feature of new IoT-native applications when performing a major upgrade to, or implementing, a new class of application.
  • Add digital twins to existing (pre-IoT era) business applications when they can be applied in a minimally invasive fashion without inflicting major disruptions on existing applications.
  • Acquire digital twins as a feature of new IoT-connected products and equipment.
  • Acquire prebuilt digital twins for use with business applications from TSPs, when available, to simplify development and reduce time to deployment.
  • Plan to develop a majority of custom discrete and composite digital twins using IoT platforms.
  • Plan to develop most DTOs using technologies specifically designed for the purpose.

Kanoo Elite is well known for doing extensive research with digital twin technologies, and an industry trend setter to evolve applications across different industries. bringing Augmented Reality (AR) into asset management. Kanoo Elite is a key player to implement digital twin systems engineering lifecycle, for your teams to streamline product design and development. We have built up a wealth of knowledge as a result of our participation in a number of successful Digital Twin collaborative projects, and we possess a natural progression to assist you to develop the perfect implementation of digital twin to transform the monitoring and maintaining of your offshore assets.

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