Module 1
Digital Engineering Strategy and the Digital Thread
- Digital engineering principles and value cases
- Authoritative sources of truth/information
- Model and data ecosystems across the lifecycle
- Interoperability, ownership and governance
Digital Engineering Programme
Digital Engineering, Simulation and Digital Twins
Connect models, simulation and operational data to improve design decisions, virtual integration and lifecycle learning.
Course overview
Create a digital engineering environment that links authoritative models, simulation and system evidence.
Digital Engineering, Simulation and Digital Twins examines how connected models, data, simulation and operational feedback can improve system development and lifecycle decisions.
Participants define the digital thread, establish model and data authority, plan simulation and virtual integration, explore digital-twin architectures and consider verification, uncertainty, configuration, cybersecurity and governance implications.
The course focuses on decision value and lifecycle integration rather than technology for its own sake, helping teams select practical digital engineering use cases and implementation priorities.
Learning outcomes
What participants will be able to do.
By the end of the course, participants should be able to:
- Explain digital engineering, digital thread, simulation and digital-twin concepts and their relationships.
- Define authoritative sources of system information and model/data ownership.
- Identify high-value simulation and virtual integration use cases.
- Plan model verification, validation and uncertainty management for decision support.
- Design a conceptual digital-twin architecture linking physical assets, models and operational data.
- Define data, interface, configuration and interoperability requirements for digital engineering.
- Assess governance, cybersecurity, trust, ethics and lifecycle risks.
- Develop a phased digital engineering implementation roadmap linked to business and engineering value.
Who should attend
Designed for professionals who contribute to complex-system decisions and delivery.
- Systems and digital engineers.
- Simulation, modelling and analysis professionals.
- Architecture, integration, verification and test teams.
- Engineering managers and technical transformation leaders.
- Asset, operations and sustainment professionals exploring digital twins.
Prerequisites
Professionally relevant and application-focused.
Participants should have practical engineering or technical-management experience. Familiarity with systems engineering or modelling is useful but no specific software platform is required.
Course structure
A five-module systems engineering learning journey.
The sequence develops understanding, application and practical engineering outputs progressively across five connected Modules.
Module 2
Simulation Strategy and Model Credibility
- Simulation use cases and decision questions
- Model abstraction, assumptions and fidelity
- Verification, validation and uncertainty considerations
- Experiment design, sensitivity and evidence
Module 3
Virtual Integration and Collaborative Engineering
- Model exchange and co-simulation concepts
- Interface testing and virtual integration
- Configuration alignment across models
- Digital reviews and multidisciplinary collaboration
Module 4
Digital Twins and Operational Learning
- Digital-twin purpose and lifecycle positioning
- Physical-digital connections and data flows
- State estimation, prediction and decision support
- Operational feedback, maintenance and continuous learning
Module 5
Governance, Cybersecurity and Implementation Roadmap
- Data/model quality and trust
- Cybersecurity, access and lifecycle assurance
- Value, cost, capability and adoption considerations
- Phased digital engineering roadmap and case review
Learning experience
Apply engineering concepts—not simply remember terminology.
Connect
Map the digital thread
Identify how models, data and evidence should connect across lifecycle decisions.
Simulate
Explore before committing
Use simulation logic to compare alternatives and expose integration or performance risk.
Twin
Define purposeful digital twins
Link physical systems and digital representations to specific operational decisions.
Govern
Maintain trusted information
Address configuration, quality, cybersecurity and accountability.
Roadmap
Prioritise implementation
Sequence use cases, capability, technology and governance around measurable value.
Completion requirements
Demonstrate participation, application and professional judgement.
- Participate in digital-thread, simulation and digital-twin workshops.
- Complete model credibility and virtual-integration exercises.
- Develop a conceptual digital-twin use case and architecture.
- Present a phased digital engineering roadmap.
Participant outputs
Leave with practical systems engineering artefacts.
- Digital-thread and information-authority map.
- Simulation strategy and credibility checklist.
- Virtual-integration use-case plan.
- Conceptual digital-twin architecture.
- Digital engineering governance checklist.
- Prioritised implementation roadmap.
Delivery options
Choose the format that fits your people and engineering environment.
Instructor-led
Live Classroom
Face-to-face delivery with facilitated discussion, system cases, engineering workshops, technical reviews and immediate feedback.
Instructor-led
Live Virtual Classroom
Interactive online delivery using collaborative workspaces, modelling activities, breakout analysis and guided application.
Flexible
Blended Learning
A structured combination of preparation, live sessions, applied assignments, engineering artefacts and follow-up application.
Organisation-specific
Corporate and In-Company
Tailored delivery aligned with organisational lifecycle processes, standards, systems, engineering roles, models and capability priorities.
Frequently asked questions
Course information and participation.
Is the course focused on a particular software platform?
No. It is technology-agnostic and focuses on engineering decisions, architectures and governance. Tool-specific demonstrations may be included for corporate cohorts.
What is the difference between a digital thread and a digital twin?
The digital thread connects lifecycle information and evidence; a digital twin is a purposeful digital representation linked to a physical system or process for defined analysis or decision use cases.
Does the course cover simulation credibility?
Yes. Participants consider model verification, validation, assumptions, uncertainty, fidelity and evidence appropriate to the decision being supported.
Does it include cybersecurity?
Yes. Digital engineering introduces information, connectivity and trust risks, so governance and cybersecurity considerations are included in the implementation model.
Can the course support a digital engineering roadmap?
Yes. A prioritised roadmap is one of the principal participant outputs and can be tailored to organisational maturity and use cases.
Take the next step
Connect engineering models, simulation and lifecycle data to better technical decisions.
Discuss public delivery, a corporate cohort or a tailored programme aligned with your organisation’s systems, lifecycle environment and engineering capability priorities.