The Digital Twin Consortium (DTC) Composability Framework provides a transformative approach to digital twin system development that facilitates greater flexibility and innovation. It ensures that key quality attributes are considered from the onset, focusing on interoperability, security and trustworthiness, scalability, and design reuse, aligning with businesses’ desired objectives and evolving needs. This article utilizes the Composability Framework to explore how organizations can effectively incorporate these and other key attributes when developing digital twins.

Composable Digital Twins

Composability in digital twins refers to constructing a modular system that can adapt over time as complexity increases. A composite digital twin is a system of assembled digital twins that allows for scaling. A composable approach allows components and capabilities to be reused for building various applications tailored to specific purposes and end users.¹ The DTC Composability Framework facilitates scalability by providing a comprehensive, structured approach to integrating interoperable components that can be readily updated, replaced, or augmented as requirements evolve. This provides the foundation for designing, developing, deploying, and operating robust digital twins.

Developed by the DTC membership, the framework is comprised of work products organized into a workflow that includes a Business Maturity Model² to assess an organization’s readiness level for digital twin adoption, the Capabilities Periodic Table (CPT)³ comprised of sixty-one elements, grouped into six categories, are technology agnostic based on a modular and adaptable approach, and the Platform Stack Architectural Framework⁴ to build on and delineate specific requisite “layers” forming a full stack platform. These components, used in conjunction, allow organizations to assess their capabilities, adopt the framework, identify and develop requisite components, and implement their digital twins in a structured and scalable manner.

Quality Attributes

Interoperability is the capability of two or more functional units to process data cooperatively.⁵ Within the DTC Composability Framework, interoperability is a foundational attribute that enables information exchange among components and related systems. The composable approach facilitates the integration of diverse components, enabling digital twin systems to communicate and exchange data effectively. Through standard data formats, communication protocols, and interfaces, these components can operate coherently for improved efficiency.

The framework addresses scalability by providing a structured approach to architecting digital twin systems. As the scope of the digital twin’s functionality expands, the modular approach enabled by the Composable Framework can minimize the need for complex simulations and more involved integration testing. Digital twins developed with scalability and interoperability can grow and evolve, avoiding potential impacts on quality.

As described above, one of the six groups contained in the Capabilities Periodic Table is Trustworthiness. In terms of digital twins, Trustworthiness is directly associated with the reliability of the virtual in representing its physical counterpart. Trustworthiness is “The degree of confidence one has that the system performs as expected. Characteristics are represented by Figure 1, and include safety, security, privacy, reliability and resilience in the face of environmental disturbances, human errors, system faults and attacks”⁶

A recent report by the National Academies of Sciences, Engineering, and Medicine highlights data quality as a significant concern for the reliability of digital twins. They also note, “In the digital twin virtual representation, verification and validation play key roles in building trustworthiness, while uncertainty quantification measures the quality of the prediction.”⁸ Trustworthiness is a major capability group in the Capabilities Periodic Table and a foundational aspect of the Platform Architectural Stack in the Composable Framework. It is essential for making informed decisions, optimizing processes, and predicting outcomes.

To address this new frontier, the DTC recently launched a Joint AI Working Group to bring together the collective intelligence of the OMG Community of Consortia and align AI-related activities across four areas of expertise within the entire community. The focus areas include Standardization and Semantics, Responsible AI and Data Provenance, Extended Reality (AR/VR/MR), and Interoperability and Intelligent Automation.

The Interoperability and Intelligent Automation Subgroup aims to develop and maintain a comprehensive framework for Interoperability, Intelligent Automation, and Gen AI using multi-agent-based systems. This framework will provide guidance for creating seamless, intelligent, and autonomous ecosystems by defining and developing the key assets needed, promoting their use in the industry, and ensuring interoperability across different platforms and technologies.⁹

Foundational Elements of the DTC Composability Framework:

Business Maturity Model

The Business Maturity Model for Digital Twins¹⁰ assesses an organization’s ability to implement and leverage digital twins effectively. It involves multiple maturity levels and evaluates factors like digital infrastructure, data management practices, and workforce skills. The stages and levels help organizations systematically plan, implement, and enhance their digital twin capabilities, ensuring alignment with business goals and technological advancements. The model helps to calibrate and provide guidance to determine whether an organization can effectively develop and utilize digital twins to achieve its business objectives.

Capabilities Periodic Table

At the core of the composability framework, the Digital Twin Capabilities Periodic Table (CPT) V1.1, provides an architecture and technology agnostic definition framework that focuses on use case capabilities requirements versus the features of the technology solution. The capabilities are organized into six categories: Data Services, Integration, Intelligence, User Experience (UX), Management, and Trustworthiness. Each category encompasses a range of capabilities essential to digital twin development and subsequent lifecycle phases. The recently released CPT Version 1.1, led by the DTC Composability Framework Subgroup, introduces enhancements, including ‘Responsibility’ as a core capability, emphasizing ethical considerations, and a new ‘Search’ capability for efficient data management. Additionally, it consolidates Augmented and Virtual Reality into Extended Reality (XR) and reclassifies temporal data stores under "Domain Specific Data Management." Structured abbreviations have been adopted for quicker reference, and detailed capability mapping aids the transition from version 1.0.

A complete Capabilities Periodic Table toolkit is provided for those looking to utilize the CPT and is publicly available at https://www.digitaltwinconsortium.org/initiatives/capabilities-periodic-table/. The toolkit includes a user guide¹¹, worksheet, and conversion table to use the toolkit spreadsheet effectively. Looking ahead, CPT Version 2.0 aims to enhance AI interoperability, integrate geospatial computing, and improve user experience. This initiative encourages community collaboration to shape future updates, highlighting the consortium’s collective effort in advancing digital twin technology.¹²

Platform Stack Architectural Framework - Architectural Considerations

The Platform Stack Architectural Framework White Paper by the Digital Twin Consortium, https://www.digitaltwinconsortium.org/platform-stack-architectural-fram-formework-an-introductory-guide-form/,outlines the essential concepts, capabilities, and components to develop digital twin systems. It discusses the foundational requirements for real-time data integration, virtual representation, and synchronization between physical and virtual entities. The paper defines criteria for qualifying digital twin systems, highlights the importance of security and IT/OT integration, and provides reference architectures for building systems-of-systems with composable twins. Additionally, it presents practical use cases across various industries and aligns the framework with other industry standards and approaches, such as RAMI Industry 4.0 and cloud-based architectures, to ensure interoperability and effective implementation.

Composable Framework Workflow

The Business Maturity Model, Capabilities Periodic Table, and Platform Stack Architectural Framework combine to provide a holistic view and Composable Framework Workflow for digital twin development and implementation focusing on key quality attributes. The workflow spans the entire Digital Twin Lifecycle, from planning to deployment to operation, maintenance, and decommissioning. When developing a digital twin, it is advisable to begin with a specific use case and determine the purpose the digital twin needs to serve for that use case. Once this is established, the data and capabilities requirements can be identified using the Capabilities Periodic Table and then mapped to the Platform Stack Architectural Framework. Technology and vendor selection can be done once these steps have been completed (Mckee, 2024). Oftentimes, this process is done in reverse, which can produce undesirable results.

A representation of this workflow is realized through the DTC Technology Showcase, a reference library of use cases/case studies that span a variety of domains, including energy, healthcare, and manufacturing: https://www.digitaltwinconsortium.org/initiatives/technology-showcase/. The Technology Showcase includes examples of capabilities and mappings to the platform stack. The Wind Farms Remote Operations Center by XMPro¹³ uses a composable approach for wind farm condition monitoring and energy prediction use cases.

As industries undergo digital transformation, Digital Twins play an important role. Leveraging the Composable Framework can help realize the potential digital twins have to transform business by providing a holistic understanding, optimized decision-making, and effective action to achieve positive results. Consortium members will continue accelerating the digital twin market by demonstrating thought leadership, innovative research, best practices, standards requirements, frameworks, architectures and use cases that consider and implement quality attributes of digital twin systems.

Future Look: The Role of GenAI in Digital Twin Composability for Quality and Trustworthiness

Infused in virtually every phase of the Digital Twin Lifecycle, Generative AI (GenAI) is driving the next stage of Digital Twin evolution. Already being utilized by DTC members in their platforms for augmenting Digital Twin Composability, “Gen AI is poised to revolutionize quality assessment and assurance in digital twin systems across their lifecycle. GEN AI will enhance requirements definition and review during planning, design and build phases, conduct design audits, generate diverse test data, and optimize code, helping improve the development process and reducing errors. GenAI is expected to contribute to runtime quality verification, predictive maintenance, and adaptive learning in the operating phase. It will likely enable intuitive user interactions through natural language interfaces and help keep the documentation current. This emerging integration of GenAI with digital twins promises to enhance overall system quality, reliability and efficiency and ultimately increase the trustworthiness of digital twin solutions across critical industries.”¹⁴