PREMISE

Despite having potential to revolutionize the product lifecycle, digital twin (DT) technology faces limited adoption in manufacturing due to:

• Uniqueness of Digital Twins: Each system is tailored, making it hard to estimate costs, benefits, and implementation specifics.
• Scope & Detail Variation: Factors like extensive data, high update frequency, and broad scope inflate costs and delay value realization.
• Lack of Standards: No established metrics or frameworks to quantify and evaluate digital twins for business decisions.

Manufacturers hesitate to invest due to difficulty in:

• Quantifying returns on investment (ROI)
• Comparing different digital twin options
• Estimating development effort and cost
• Mitigating investment risks
• Interoperability issues due to different systems

Thus, there is not only the need for standardization in the process engineering industry but also utilize a robust reference architecture to enable the creation of a playbook that would guide in implementations of Digital Twin initiatives.

This solicitation for Digital Twin for X by MxD is a precursor for such underpinnings for smart manufacturing. The possibility of Digital Twins (DT) to impact the manufacturing industry is immense and spans the entire product lifecycle from design to sustainment. However, no two digital twins are exactly alike making them difficult to quantify in business terms. To provide value, a given DT must match the fidelity and scope of the model to the type of problem being solved. Building on the Digital Twin Consortium (DTC) framework, this project call will fund the creation of two framework-compliant DT’s – one at a manufacturer and one within MxD’s Future Factory.

The objectives are to:

• Demonstrate digital twin capabilities across data, integration, intelligence, user experience, management and trustworthiness.
• Match digital twin fidelity to the problem statement and curate minimum viable datasets.
• Develop a methodology to estimate level of effort based on DTC capability maturity levels.
• Document the implementation in a reusable case study.

In this solution brief we provide the basis to build a comprehensive playbook for Digital Twin creation utilizing:

1. the standards provided by the Digital Twin Consortium, and
2. Numorpho’s reference architecture that would enable the composition of the blueprint.

In chemistry terms, this would relate to utilizing the periodic table to formulate molecules, compounds and composites to enable unique material properties and interactive reactions.

TABLE OF CONENTS

• Types of Digital Twins
• The DTC Framework
• Our Perspective – The DTWM Reference  Architecture
• Our Solution
  • Executive Summary
  • Objectives
  • MxD Relevance
  • Use Case Definition
  • Whitepaper Details
• Program Plan
• Transition Plan
• Summary

TYPES OF DIGITAL TWINS

To simplify the broad range of potential digital twin applications, a classification approach is used called the “5 P’s“. This model is easy to remember and covers nearly all use cases of industrial digital twins:

1. Part Digital Twin: Digital representation of individual components or parts typically to understand the physical, mechanical, and electrical characteristics of the part. This allows companies to monitor, analyze, and predict the performance and health of that particular part, optimizing maintenance schedules and extending its lifecycle.
2. Product Digital Twin: Digital representation of the interoperability of components or parts as they work together as part of a product. This enables companies to simulate and test product behavior under various conditions, improving design, ensuring quality, and speeding up the time to market.
3. Plant/Platform Digital Twin: Digital representation of a plant, facility, or system to understand how assets work together at an operational level. This allows businesses to enhance operational efficiency, reduce downtimes, and optimize production processes through real-time insights and predictive analytics.
4. Process Digital Twin: Digital representation of a specific process or workflow within a system or a facility. This helps companies refine and optimize processes, identify inefficiencies, and ensure smoother and more cost-effective operations.
5. Person Digital Twin: Digital representation of a person to capture their movements, habits, interactions, skills, knowledge, and preferences. This helps companies gain insights into workflow patterns, fatigue patterns, and safety concerns ensuring increased productivity and a reduction in workplace-related injuries.

THE DTC FRAMEWORK

The Digital Twin Consortium (DTC) defines a digital twin as “a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.” This definition is accompanied by a “periodic table” map of potential Digital Twin capabilities as an architecture- and technology-agnostic requirements mapping tool to assist organizations in designing from a “use case” perspective rather than a feature/solution focus. Their digital twin platform stack reference architecture builds off these capability categories and links them to the IT/OT systems which enable them.

The DTC’s Digital Twin Capabilities Periodic Table (CPT) is a comprehensive framework that categorizes and organizes the key capabilities associated with digital twins. It serves as a reference guide for understanding the diverse functionalities that digital twins can encompass.

The CPT is structured like a periodic table, with elements organized into rows and columns based on their characteristics. The key highlights of the Digital Twin Capabilities Periodic Table include:

• Element Categories: The CPT classifies digital twin capabilities into different elemental categories, each representing a specific aspect of functionality.
• Functional Elements: Within each category, there are functional elements that represent specific capabilities or features associated with digital twins.
• Interconnectivity: The table illustrates how these functional elements are interconnected, showcasing the relationships and dependencies between different capabilities.
• Comprehensive Coverage: The CPT aims to provide a comprehensive overview of digital twin capabilities, covering aspects from modeling and simulation to analytics, data integration, and more.
• Standardization Efforts: It reflects the ongoing efforts towards standardization in the digital twin domain, helping establish common definitions and frameworks for better interoperability.
• Guidance for Implementation: The CPT serves as a valuable resource for organizations looking to implement digital twin technology by offering a structured and organized view of the diverse capabilities available.

The Digital Twin Capabilities Periodic Table is a tool that aids in understanding, categorizing, and navigating the various functionalities associated with digital twins, contributing to the development and adoption of standardized practices in the industry.

The Capabilities Periodic Table (CPT) (shown below) from the Digital Twin Consortium is a perfect elemental representation for enacting such orchestrations using their groupings to manage the flow of information and the interactions of people, processes and technologies.

The groupings of the periodic table are represented below:

Version 1/1 of the Periodic Table is depicted below:

OUR PERSPECTIVE

Using our forays in digital engineering, asset management, simulations, generative AI and AR/VR, we at Numorpho Cybernetic Systems (NUMO) plan to create a compelling blueprint for effecting such transformations that would merge the digital and the physical realms.

Our Digital Twine World Model (DTWM) Reference Architecture is the appropriate tool to articulate the end-to-end interactions between the systems in a manufacturing enterprise. It will help represent the conjoining of digital information and physical processes to enable “Digital Twin for X” use cases to be blueprinted, developed and implemented by coordinating a diverse set of constituencies from stake holders, operators, tool providers and platform integrators. We look at the entire process from upstream where products are developed, midstream where production is done and downstream for support, maintenance and even marketing and sell opportunities to connect the dots between systems using integrated services to pull and push information dynamically based on intent.

Depicted below is the representation of the Future Factory Digital Twine that will enable to theme out Digital Twin compositions for pertinent initiatives to coordinate digital and physical activities for activities related to part, person, product, plant, and/or process.

It contains the elemental groupings prescribed by the Capabilities Periodic Table of the DTC and additionally includes Planning and Governance, and Product Engineering, two key business drivers that are also needed to correctly manage the mélange of physical and digital processes in Digital Twin articulations:

1. Planning and Governance – Capabilities that enable organizational alignment, portfolio management, road-mapping, and governance of Digital Twin initiatives.
2. Management – System and ecosystem management capabilities.
3. Product Engineering – Capabilities to support product engineering and manufacturing process activities leveraging the Digital Twin.
4. Integration – Enables data access to existing internal and external enterprise systems and applications. Enables communication across different digital twins.
5. Data Services – Enables data access, ingestion and data management across the platform from the edge to the cloud. It establishes the physical to virtual connection and receives data directly from equipment sensors or control systems, performs localized processing, and distributes to other tiers.
6. Intelligence – Provides an environment for the development and deployment of industrial Digital Twin solution. It provides the services for data integration, basic and advanced analytics, AI, orchestration, and other Digital Twin process capabilities.
7. UX – Provides the user with the ability to interact with Digital Twins and visualize its data.
8. Trustworthiness – Security, privacy, safety, reliability, and resilience capabilities.

OUR SOLUTION

“Digital Twin for X” is a concept that involves creating a virtual replica of a physical object or system.

1. We will utilize the Digital Twin Capabilities Periodic Table (CPT) to design, develop, deploy and operate digital twins based on use case capability requirements vs the features of technology solutions.
2. Our DTWM reference architecture will help blueprint the initiative by coordinating upstream, midstream and downstream capability digital threads and interweaving them into connected dots where people, process and technology interact to drive requirements.
3. We will utilize MxD’s Sensor Kit to retrofit the physical model to enable interaction with the digital twin to enable brownfield re-engineering, greenfield scale up and blue sky initiatives to be enacted.

We propose a Future Factory Digital Twine (FFDT) that intelligently corresponds information end-to-end to digitally thread and enact appropriate solutions by:

• Advancing Established Standards: Encourage adoption of existing frameworks like those from the Digital Twin Consortium (DTC).
• Gathering Real Implementations: Document and share successful case studies to provide tangible examples.
• Develop Practical Tools: Build user-friendly frameworks for estimating costs, effort, and value of digital twins.

The FFDT will showcase the art of the possible coupled with the science and math of engineering to digitally represent different interacting digital twins on MxD’s future factory floor. By addressing uniqueness, defining metrics, and creating practical tools, the adoption of digital twins in manufacturing can be significantly accelerated, unlocking its transformative potential.

• The digital twins will be architected using the DTC Platform Stack architecture as a reference model covering the IT/OT platform, virtual representations, service interfaces, and applications.
• We will utilize our DTWM Reference Architecture to blueprint the use case on MxD’s Factory floor. The components of the DTWM will mirror the capability categories in the Digital Twin Capabilities Periodic Table.

Our solution will showcase the art of the possible coupled with the science and math of engineering to merge the physical and the digital. The four key tenets to enable the solution are:

1. Integration – the ability to connect different systems and data sources to create a comprehensive digital twin.
2. Interoperability – the ability of different systems to work together seamlessly, regardless of their underlying technology or architecture.
3. Synchronization – the ability to keep the digital twin up to date with the physical object or system in real-time.
4. Composability – the ability to create a digital twin by combining smaller, modular components.

Key Capabilities of our solution will be:

• Bidirectional synchronization mechanisms with real-time industrial data leveraging data integration.
• Interoperable data exchange between digital twins using DTDL schemas.
• Common semantic data models aligned with industry ontologies.
• Containerization and microservices architecture for loose coupling.
• Cloud and edge processing to meet latency and autonomy requirements.
• Visualization and analytics applications built on digital twin data.
• Security and governance based on trustworthiness principles of privacy, safety and reliability.

Executive Summary

In current industrial operations, lack of accurate information leads to incomplete situational awareness. Futuristic smart factories must have accurate and real-time geospatial and process information. To address these challenges and improve efficiency, there is a need to provide real-time localization and interaction technology, a digital twin.

DEFINITION: Numorpho’s Digital Twine Reference Architecture is an end-to-end orchestration of digital threads (information flow) connecting people, processes, and platforms to enable the intelligent digital-physical interactions of digital twins.

The Future Factory Digital Twine (FFDT) will provide guidance for architecting digital twins that follow standards, industry best practices, the Digital Twin Consortium (DTC) framework and Numorpho’s DTWM reference architecture.

The FFDT will demonstrate the art of the possible by engineering a system to digitally represent different interoperable digital twins on MxD’s future factory floor. This will enable simulation, emulation, interaction, calibration and operation of physical systems by addressing uniqueness and scope, utilizing standards, and creating practical dashboards and analytics tools. Through this process, the adoption of digital twins in manufacturing can be significantly accelerated, unlocking its transformative potential to enable Industry 4.0 and beyond.

In this solution brief we provide the basis to build a comprehensive playbook for Digital Twin creation utilizing:

1. the standards provided by the Digital Twin Consortium,
2. Numorpho’s reference architecture that would enable the composition of the blueprint, and
3. MxD’s Sensor Kit.

In chemistry terms, this would relate to utilizing the periodic table to formulate molecules, compounds and composites to enable unique material properties and interactive reactions, albeit in an industrial setting

Objectives

The project has the following objectives:

1. Architect a Digital Twin System Using DTC Framework – Determine a manufacturing industry use case or identify a system/subsystem of interest within MxD’s Factory Floor. Demonstrate all major Digital Twin capability categories: Data Services, Integration, Intelligence, User Experience, Management, and Trustworthiness.
2. Implement the Digital Twin System – Document the problem to be solved or the system to be understood, including baseline levels of performance. Develop a structured methodology for deciding upon system boundaries, curating minimum-viable datasets, and matching fidelity to problem statement. Develop, integrate, and test the DT system to validate hypotheses.
3. Devise a Level of Effort Methodology – Using project results and prior expertise, develop a coarse methodology to provide manufacturers an estimate of the level of effort for their novel use. This methodology should align to the DTC framework of capabilities and maturity levels and incorporate decisions made about scope, fidelity, and frequency.
4. Develop a Case Study for Technology Showcase – Incorporate project learnings into a Case Study in existing DTC Tech Showcase format.

MxD Relevance

We will utilize best practices from some of MxD’s prior SIP projects that we have participated in:

• MxD-22-05 INTERACTIVE DIGITAL TWINS PLAYBOOK BUILDER – A playbook that simplifies a complex technical goal into a step-by-step implementation guide.
• MxD-22-03 DIGITAL MANUFACTURING PLAYBOOK – Lift-and-shift operations to include a digital framework based on the maturity and needs of the company to enable brownfield, greenfield and bluesky implementations.
• MxD-22-01 TECHNICAL DATA AS A SERVICE – Enable asset management and connected digital manufacturing thru the entire cycle of make, manage, move, market and maintain activities.
• MxD-21-14 PREDICTIVE MAINTENANCE IN MANUFACTURING – Enable proactive troubleshooting and management of equipment using condition-based or AI-based maintenance techniques rather than schedule-based Mean Time Between Failure historical analysis.
• MxD-21-01 AI DESIGN ADVISOR – A digital advisor that back feeds lessons learned and best practices from manufacturing to product development.
• MxD’s SENSOR KIT – Utilize the array of sensors that MxD has championed for operational management.

Other key MxD thesis we will avail of are: Prior SIP projects, Cybersecurity, Supply Chain Management and Factory Modernization.

Use Case Definition

Whitepaper Details

Our solution will concern itself to Option #1 of MxD’s Digital Twin for X RFP (24-01) – MxD Future Factory. A description of this option is below:

Digital twin implementations on MxD’s Future Factory floor should focus on the objectives of education, the “Art of the Possible,” and interactivity/hands-on learning. It should contain a dynamic visual component to engage the visitor’s physical intuition.

The potential use cases are:

• Production Scaling / Remote Commissioning Digital Twin
• Equipment or Process Health Digital Twin In-a-Box
• Equipment or Process Health Digital Twin Mass Customization / Personalization
• Sustainability
• Complex Systems with Unobservable Behavior

We propose two whitepapers as potential solutions:

1. MODEL A – Interact real time with the table-top models of smart manufacturing solutions like those represented in the showcase areas of Betacom-IngramMicro-Google, Siemens and EY at MxD’s Future Factory floor to visualize the workings of the smart factory initiatives. For example, Betacom-IngramMicro-Google has 4-5 different types of smart manufacturing set-ups that we would use as the initial basis to theme out the workings of a process or platform digital twin. We would be able to show normal operating conditions (everything green) with equipment run details, holistically represent what happens to the flow when things go wrong (red, purple implications), and proactively take corrective actions to resolve issues.
2. MODEL B – (Ram Shetty to provide details) tools play to utilize PTC’s Thingworx, Kepware and Vuforia or equivalent Siemens tools (Mindsphere, Mendix and Simcenter) to showcase a process, part or product driven digital twin.

The first step would be to virtualize the entire MxD factory floor using Matterport’s 3D rendering of the space and enable its dynamic navigation to the different waypoints on the floor space to visualize the different setups therein. Here is an example of how this would work.

PROGRAM PLAN

Phases	Delivery Format	Description	Due Date (Month #)
Phase 1 – Constitute	PPT, Excel, Word, Code, HW	Gather requirements, define and design the foundation blocks	Month 3
Phase 2 – Institute	PPT, Excel, Word, Code	Build out the Model and the interacting elements	Month 6
Phase 3 – Operate	PPT, Excel, Word, Code, HW	Deploy the solution	Month 9
Test & Validate	PPT, Excel, Word, Code, HW	Complete Functional and User Acceptance testing with Continuous Improvement	Month 12

KPIs, metrics and measures

Facilities and Equipment

Risks

TRANSITION PLAN

1. Assessment and Readiness Check
2. Goal Definition and Strategy Development
3. Pilot Testing
4. Infrastructure Preparation
5. Training and Education
6. Deployment and Integration
7. Monitoring and Evaluation
8. Continuous Improvement
9. Change Management and Support
10. Documentation and Knowledge Sharing

In this solution brief:

• MODEL A proposed the creation of a high-level digital twine to showcase the “art of the possible”. Two tabletop systems will be used to create digital twins to interact with the physical flow of operations. Enabling steady running states and pushing the boundaries of the system to determine issues that might happen and discover the corrective actions that could be taken.
• Our other whitepaper, (MODEL B), represented the creation of Digital Twin in a box.

Post the successful completion of the project, we will develop a playbook to implement these at other showcase representations and the modular utilization of simulation, MES and IoT tools from Siemens, PTC, Rockwell and GE to enable plug-and-play functionality for different SMMs.

SUMMARY

The Future Factory Digital Twine would be the perfect playbook to showcase the art of the possible for design to manufacturing by relating to new processes that are showcased in the factory floor by creating an interactive viewport of the shop where each way point is tagged to invoke a specific digital twin of that process/initiative. It would work as a blueprint to create real digital twins in actual settings by utilizing the rigor and connected dots of Numorpho’s DTWM reference architecture.

We invite like-minded companies to join us in this adventure and believe that together we can create a compelling guidebook for future engineering. We are based in the Chicagoland area and members at MxD and mHUB, two amazing organizations that have helped foster our development and the progression of our thesis for enabling additive manufacturing, automation, smart technologies, and the future of machine and actionable intelligence.

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

References:

• 20221011: What is a Digital Twin? – EVERYTHING CONNECTED – Numorpho’s Book of Business
• Knitting the Cybernetic Fabric – EVERYTHING CONNECTED – Numorpho’s Book of Business
• The Digital Twine Demystified – EVERYTHING CONNECTED – Numorpho’s Book of Business
• AUTOMATE – Our Digital Twine Reference Architecture – EVERYTHING CONNECTED – Numorpho’s Book of Business
• Smart Manufacturing – Walking the Talk – EVERYTHING CONNECTED – Numorpho’s Book of Business
• SMART MANUFACTURING – EVERYTHING CONNECTED – Numorpho’s Book of Business