2 - Fundamentals of Digital Engineering Systems

This is the 2nd in a 3-part series on Digital Engineering. The 1st introduced the Capabilities of Digital Engineering.

The Digital Engineering deployment Program starts with a Digital Model of the desired capabilities for the specific domain, developing depictions of the system supporting the Program uses, including requirements analysis, architecture, design and cost trades, design evaluations, optimizations, system, subsystem, component, and subcomponent definitions and integration; cost estimations; training aids and devices development; developmental and operational tests; sustainment and disposal. These models and simulations are used, to the greatest extent feasible, in systems engineering and Program/Program risk management, cost and schedule planning, and providing critical capabilities effectively addressing issues in areas including but not limited to interoperability, joint operations, and systems of systems across the entire acquisition life cycle.

The program manager is responsible for planning and coordinating the Programs’ use of models, simulations, tools, data, data rights, and the engineering environment. The performance of the actual tasks starts with delegation to the Systems Engineer and other Program staff as appropriate.

The Program identifies and maintains the model-centric technology, methodology/approach, and usage, preferably in digital format (e.g., Digital System Models). It integrates the authoritative technical data and associated artifacts stakeholders generate throughout the system life cycle. Unless impractical, the Program will develop the Digital System Models using standard model representations, methods, and underlying data structures.

Digital System Models are the collaborative product of Systems and Design Engineering efforts. The Program constructs them by integrating data consumed and produced by the activities across the Program. The Digital System Models include the technical baseline, parametric descriptions, behavior definitions, internal and external interfaces, form, structure, and cost. This data is traced, at minimum, from operational capabilities through requirements, design constructs, tests, training, and sustainment. The Program validates the Digital System Model baseline at technical milestones.

Systems Engineers use these models to define, understand, evaluate, communicate, and document Program scope and maintain an “authoritative source” about the system. When captured digitally, the system model produces technical documentation and other artifacts supporting Program decisions. A properly managed, digitally based system model is expected to be more accurate, consistent, and sharable.

The models, Simulations, Tools, Methodologies, and Data employed in the acquisition activities will have an established level of trust, and the Program will use them with an acknowledged level of risk appropriate to the application. The development of models, construction of simulations, and use of these assets to perform Program definition and development activities require collaboration among all program stakeholders, led by the Systems Engineering team.

The Program Office ensures sufficient training in using models, simulations, tools, data, and the engineering and acquisition environment appropriately. The Program identifies metrics showing the link between training and the appropriate use of activities, which benefits the Program, especially in early defect identification, cost avoidance, and risk reduction.

The Program updates the Digital System Models throughout its life cycle, maintaining configuration management (e.g., version controls). These updates provide continuity among all Program stakeholders, including the Program model developers, simulation users, and other engineering and Program management activities.

Managing the Digital Engineering system deployment requires careful orchestration of technical, organizational, and human factors. Key considerations include establishing robust data governance frameworks, ensuring system interoperability with existing tools, providing thorough training for end users, and implementing clear change management procedures. Regular monitoring and feedback loops will be established to quickly identify and resolve issues, while documentation and support systems will be readily available to maintain operational continuity. Success ultimately depends on balancing technical excellence with user adoption, requiring strong program management and a deep understanding of the organization's engineering needs and culture.