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Digital Engineering

Digital engineering (DE) (also known as model-based engineering or model-based systems engineering) is an initiative developed and championed by ODASD(SE) to help streamline the way defense programs collect, retain, and share data. ODASD(SE) asserts that digital engineering has the potential to promote greater efficiency and coherence in defense programs by ensuring stakeholders have access to accurate, relevant, and consistent information throughout the life of a program.

To further this effort, ODASD(SE) has chartered the Digital Engineering Working Group (DEWG) whose participants represent different segments of the acquisition community (i.e., Program Executive Offices, Program Manager engineering, and science and technology proponents). The DEWG will help promote digital engineering principles throughout the Services and in other government agencies. It will explore ways to transfer traditional acquisition processes to a digital model-centric environment, and it will develop and implement the digital engineering concept across engineering functions and within the Defense Acquisition System.

Evidence across the Services and industry has affirmed digital engineering as a contemporary approach to advance defense acquisition engineering in an environment of increasing global challenges.

The Weapon Systems Acquisition Reform Act of 2009, which aimed to improve Pentagon processes in the purchase of major weapon systems, emphasized performing sound systems engineering to help programs succeed. To serve this mandate, the Department must practice systems engineering efficiently and effectively to provide the best advantage for integrating engineering efforts on a project.

Operating from a single source of truth and placing models as the lead digital artifacts move us in this direction. ODASD(SE) believes that the use of models, simulations, and digital engineering places a greater focus on the rigor and discipline needed in performing high-quality systems engineering.

Challenges and Goals

Defense programs are increasingly complex. Large systems of systems may involve multiple geographically distributed stakeholders, sometimes with competing priorities and interests. Programs involve ever-greater levels of technology, software, and requirements for both capability and security.

To stay ahead of the demands, the Department of Defense must continually scrutinize its approach to acquisition and systems engineering, including its methods for modeling and simulation that support the acquisition process. Although programs already employ models to support program activities and much of the data already is in digital form, the Department encounters significant challenges in collecting, managing, and analyzing the large amount of data required by the engineers and other stakeholders involved in the program’s development and leadership.

Programs may accumulate multiple versions of data, or stakeholders may have questions regarding the most current definitions of different forms of data. Programs may need to share data across engineering functions, leading to potential duplication of effort or work products that are out of sync with one another. In addition, programs and organizations may take varying approaches to preserving knowledge from program to program or among phases of the acquisition life cycle for a given program.

All of these challenges are here today and understandable given the natural fluidity of data and the rapid pace of change. ODASD(SE) promotes digital engineering concepts as a way to harness the power of the information available to the Department and to make that data more useful and more readily accessible across all the elements of the Department.


The following definitions relating to digital engineering are drawn from the Defense Acquisition University (DAU) and the Defense Federal Acquisition Regulation Supplement (DFARS):

Digital Artifact: The artifacts produced within, or generated from, the digital engineering ecosystem. These artifacts provide data for alternative views to visualize, communicate, and deliver data, information, and knowledge to stakeholders.

Digital Engineering: An integrated digital approach that uses authoritative sources of systems' data and models as a continuum across disciplines to support lifecycle activities from concept through disposal.

Digital Engineering Ecosystem: The interconnected infrastructure, environment, and methodology (process, methods, and tools) used to store, access, analyze, and visualize evolving systems' data and models to address the needs of the stakeholders.

Digital Model-Centric Engineering (DMCE): The application of engineering practices through the use of digital environments and tools. DMCE enables practitioners to engineer systems using digital practices and artifacts in a collaborative environment, creating a digitally integrated approach using a federated single source of truth to evolve complex systems. A primary characteristic of this environment and approach is the digital authority’s ability to capture pedigree of all system-related data to facilitate and automate traceability, show dynamic relationships and changes to various aspects of the system development, and support decision makers to make informed decisions.

Digital System Model: A digital representation of a defense system, generated by all stakeholders, that integrates the authoritative technical data and associated artifacts, which defines all aspects of the system for the specific activities throughout the system life cycle. (DAU Glossary)

Digital Thread: An extensible, configurable, and component enterprise-level analytical framework that seamlessly expedites the controlled interplay of authoritative technical data, software, information, and knowledge in the enterprise data-information-knowledge systems, based on the Digital System Model template, to inform decision makers throughout a system's life cycle by providing the capability to access, integrate, and transform disparate data into actionable information. (DAU Glossary)

Digital Twin: An integrated multiphysics, multiscale, probabilistic simulation of an as-built system, enabled by Digital Thread, that uses the best available models, sensor information, and input data to mirror and predict activities/performance over the life of its corresponding physical twin. (DAU Glossary)

Technical Coherence: The logical traceability of the evolution of a system's data and models, decisions, and solutions throughout the lifecycle.

Technical Data: Recorded information, regardless of the form or method of the recording, of a scientific or technical nature (including computer software documentations). The term does not include computer software or data incidental to contract administration, such as financial and/or management information. (DFARS 252.227-7103(a)(15))


Following are excerpts of DoDI 5000.02 policy relating to digital engineering:

  • ENCLOSURE 3, Section 9. Modeling and Simulation: The Program Manager will integrate modeling and simulation activities into program planning and engineering efforts. These activities will support consistent analyses and decisions throughout the program’s life cycle. Models, data, and artifacts will be integrated, managed, and controlled to ensure that the products maintain consistency with the system and external program dependencies, provide a comprehensive view of the program, and increase efficiency and confidence throughout the program’s life cycle.

    (5) Ensure that all test infrastructure and/or tools (e.g., models, simulations, automated tools, synthetic environments) to support acquisition decisions will be verified, validated, and accredited (VV&A) by the intended user or appropriate agency. Test infrastructure, tools, and/or the VV&A strategy including the VV&A authority for each tool or test infrastructure asset will be documented in the TEMP. Program Managers will plan for the application and accreditation of any modeling and simulation tools supporting DT&E.


    Use of Modeling and Simulation. Models or simulations that utilize or portray threat characteristics or parameters must have that portrayal accredited by the Defense Intelligence Agency. Every distinct use of a model or simulation in support of an operational evaluation will be accredited by an OTA, and, for programs under DOT&E Oversight, its use for the operational evaluation will be approved by DOT&E.

    c. Data Management, Evaluation, and Reporting
    (6): Test agencies will provide the DoD Modeling and Simulation Coordination Office with a descriptive summary and metadata for all accredited models or simulations that can potentially be reused by other programs.


    a. The program’s Product Support Manager (PSM) will assess logistics as a focused part of the program’s Program Support Assessments and technical reviews (e.g., systems engineering, test) to ensure the system design and product support package are integrated to achieve the sustainment metrics and inform applicable modeling and simulation tools.


Defense Acquisition Guidebook, Chapter 4, Modeling and Simulation, September 2013.

Systems Engineering Digital Engineering Fundamentals

Papers and Presentations

Digital Model-based Engineering: Expectations, Prerequisites, and Challenges of Infusion
Model-Based Systems Engineering (MBSE) Infusion Task Team, Interagency Working Group on Engineering Complex Systems (IAWG), March 2017

Digital Engineering Transformation Across the Department of Defense
Tracee Gilbert, Ph.D, under contract with Office of the Deputy Assistant Secretary of Defense for Systems Engineering, 2017

A Framework for Developing a Digital System Model Taxonomy
Philomena Zimmerman, 18th Annual NDIA Systems Engineering Conference, Springfield, VA, October 28, 2015

Digital System Model Development and Technical Data
Philomena Zimmerman, 17th Annual NDIA Systems Engineering Conference, Springfield, VA, October 30, 2014

A Review of Model-Based Systems Engineering Practices and Recommendations for Future Directions in the Department of Defense
Philomena Zimmerman, 2nd Systems Engineering in the Washington Metropolitan Area (SEDC 2014) Conference, Chantilly, VA, April 3, 2014

A Case Study to Examine Technical Data Relationships to the System Model Concept
Tracee Walker Gilbert, Ph.D., 16th Annual NDIA Systems Engineering Conference, Arlington, VA, October 31, 2013

Understanding and Delivering the System Model
Philomena Zimmerman, 16th Annual NDIA Systems Engineering Conference, Arlington, VA, October 31, 2013

Supporting Better Buying Power (BBP)

In addition to influencing policy and guidance, the move toward digital engineering has the potential to address BBP 3.0 in the following areas:

  • Improve tradecraft in acquisition of services with improved productivity of contracted engineering and technical services.
  • Strengthen organic engineering capabilities through disciplined and consistent application of engineering through the application of modeling, simulation, and digital engineering.
  • Improve identification, understanding, and mitigation of risks through quantitative analysis, quality data, a more connected and informed Integrated Product Team, quickly finding patterns and improved insight and giving the program a better lens to look for defects early in a program’s life cycle.
  • Streamline documentation requirements and staff reviews.
  • Remove unproductive requirements imposed on industry.

Collaboration with Industry

DoD has teamed with the NDIA Systems Engineering Division's Modeling and Simulation Committee and the INCOSE Model-Based Systems Engineering Initiative to broaden the digital engineering community and advance the practice of digital engineering across the DoD.

For additional information about the DoD digital engineering initiative, contact the ODASD(SE) DE staff.

Papers and briefings are reprinted with permission from: IEEE Systems Council | International Council on Systems Engineering (INCOSE) | National Defense Industrial Association (NDIA).