REFERENCE

• Digital Twin Consortium Unveils Updated Definitions

Using its Capability Periodic Table (CPT), the Digital Twin Consortium (DTC) has been appropriately and responsibly evolving the standards for the digital twin to enable smart manufacturing and the progression of Industry 4.0.

Groupings

DIGITIAL TWIN

Here is their new definition of the digital twin:

A digital twin is an integrated data-driven virtual representation of real-world entities and processes, with synchronized interaction at a specified frequency and fidelity. (ET = Engineering Technology):

• Digital Twins are motivated by outcomes, driven by use cases, powered by integration, built on data, enhanced by physics, guided by domain knowledge, and implemented in dependable and trustworthy IT/OT/ET systems.
• Digital Twin Systems transform business by accelerating and automating holistic understanding, continuous improvement, decision-making, and interventions through effective action.
• Digital Twin Systems are built on integrated and synchronized IT/OT/ET systems, use real-time and historical data to represent the past and present, and simulate predicted futures.
• Digital Twin Prototypes use data to model and simulate predicted futures before being integrated into IT/OT/ET Systems and before synchronization with the real-world entity or process.

The Digital Twin Consortium’s definition of a digital twin emphasizes its role as a data-driven virtual representation of real-world entities and processes. It highlights key elements that contribute to the development and implementation of digital twin systems. To better understand these elements, we can refer to the Capabilities Periodic Table (CPT) provided by the consortium:

• Outcome-driven: Digital twins are developed with specific goals and outcomes in mind, focusing on addressing business needs and challenges.
• Use case-driven: Digital twins are designed to solve particular problems or address specific scenarios, enabling businesses to make informed decisions and improvements.
• Integration-powered: The power of digital twins lies in their ability to integrate data from various sources, including IT, OT, and ET systems, creating a comprehensive representation of the real world.
• Data-built: Digital twins rely on both real-time and historical data to accurately represent the past, present, and future states of entities and processes.
• Physics-enhanced: The use of physics-based simulations and models enhances the accuracy and fidelity of digital twins, enabling businesses to predict outcomes and optimize processes with greater confidence.
• Domain knowledge-guided: The implementation of digital twins is guided by domain knowledge and expertise, ensuring that they accurately represent the intricacies of the real-world systems they mirror.
• Dependable and trustworthy systems: Digital twins are built on reliable IT/OT/ET systems, ensuring that they can be trusted for decision-making and continuous improvement initiatives.

In summary, the Digital Twin Consortium’s definition and the Capabilities Periodic Table provide a comprehensive understanding of the core elements that make up a digital twin system. These elements work in synergy to create a powerful tool for businesses to optimize their processes, accelerate decision-making, and drive continuous improvement.

DIGITAL THREAD

Here is the new definition of the digital thread:

A digital thread is a bidirectional, dependable and trustworthy interconnected information system that links multiple dimensions, including structure, behavior, space, time, and lifecycle stages.

“This revised digital twin definition emphasizes synchronization and data, with a model-based approach tied to engineering technology.

Grounded in physics, it supports the full life cycle from simulation to decommissioning digital twins.

By refining this definition, we enable more accurate, real-time representations, leading to better decisions, improved efficiencies, and deeper insights across industries.”

The groundwork for digital twin has been laid through digital thread and Industrial Internet of Things (IIoT) technology. Coupled with increasingly powerful analytics and simulation capabilities common in industrial enterprises, digital twin use cases are being adopted across the value chain. From engineering to operations and service, digital twins deliver significant business value to industrial leaders today.

For us at Numorpho Cybernetic Systems (NUMO), digital twins are found at intersections of value driver digital threads where people, processes and technologies conjoin to represent an artifact of the flow of information in an enterprise ecosystem.

This update by DTC is timely with our advances to our digital twine reference architecture (DTWM) that utilizes Large World Models (LWM) to engineer and represent object interactions using geo-spatial orientation, queuing theory, multi physics simulations and actionable intelligence for a virtual end-to-end view of all aspects of the process from macro to micro.

Based on Large World Models we are building an intelligent geo-spatial representation for engineering processes called the Digital Twine Reference Model (DTWM). How can this new definition of digital threads and digital twins help us orchestrate automation upstream, midstream and downstream activities to enable resiliency in manufacturing and other process related domains?

The Digital Twin Consortium’s definition of a digital thread highlights its role in creating a connected and trustworthy information system that links various dimensions, such as structure, behavior, space, time, and lifecycle stages. This interconnectedness is crucial for enabling resiliency in manufacturing and other process-related domains, particularly when combined with the concept of Large World Models and the Digital Twine Reference Model (DTWM).

Here’s how the digital thread and digital twin concepts can help orchestrate automation and enhance resiliency across upstream, midstream, and downstream activities:

1. Data and model management: A digital thread ensures secure and resilient data exchange and interoperability, facilitating the integration of different data types and models across the manufacturing process. This enables better decision-making and process optimization based on real-time, accurate data.
2. Ontologies and information flow: By providing ontologies and fostering a seamless flow of information, digital threads help connect data and models throughout the lifecycle of a digital twin. This ensures that upstream, midstream, and downstream activities are aligned and informed by the most up-to-date information.
3. Visibility and transparency: Digital threads enhance visibility and transparency across the manufacturing process, enabling organizations to identify potential bottlenecks, inefficiencies, or risks. This visibility allows for proactive interventions and informed decision-making to ensure process resiliency.
4. Interconnected systems: Digital threads facilitate closed-loop communication between isolated data, repositories, and systems, ensuring seamless integration and coordination across products, people, processes, and places. This interconnectedness helps optimize operations, reduce costs, and improve overall efficiency in manufacturing.

In the context of Large World Models and DTWM, digital threads serve as the foundation for creating an intelligent geo-spatial representation of engineering processes. By leveraging the capabilities of digital threads, organizations can build resilient and efficient manufacturing processes that adapt to changing conditions and requirements, ultimately driving growth and competitiveness in the digital age.

Stay tuned for more development on this at our book of business (Numorpho.org) website and in the comments section.

Pertinent prior press releases by DTC:
– https://lnkd.in/gWjhPMXe

Krishna Kumar, Fastway Engineering, Giuseppe Padula

NITIN UCHIL Founder, CEO & Technical Evangelist
nitin.uchil@numorpho.com