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Digital Engineering

Digital engineering (DE) (closely related to model-based engineering or model-based systems engineering) is an initiative championed by ODASD(SE). DE is intended to help streamline the way the DoD designs warfighting systems, conducts design trade-off analyses, and collects, retains, and shares data via models (which take the form of data, process, and/or algorithm), with increased use of interoperable engineering tools and virtual environments in the design process. ODASD(SE) asserts that digital engineering has the potential to promote greater efficiency, increased coherence, and reduced risk in defense programs by ensuring stakeholders have access to accurate, relevant, and consistent information, coupled between technical and programmatic, throughout the life of a program.

Digital engineering evolved to the current concept through increased application of modeling and simulation efforts in traditional acquisition engineering activities, coupled with increased use of advanced tools and techniques in computational science. Previous and ongoing efforts of the Defense Modeling and Simulation Coordination Office (DMSCO), and the related Acquisition Modeling and Simulation community are still valid within a range of uses and in many cases are evolving to be a part of digital engineering. Use of modeling and simulation in engineering, or in engineering support to acquisition, is now covered by digital engineering.

To collaboratively further the digital engineering effort, ODASD(SE) has chartered the Digital Engineering Working Group (DEWG) whose participants represent different segments of the engineering and acquisition communities (e.g., Program Executive Offices, Program Managers, engineering, and science and technology proponents). The DEWG promotes digital engineering principles throughout the Services and in other government agencies and can assist those elements in advancing the digital engineering practices within their organization. It is collaboratively assessing, promoting, and appropriately increasing the use of digital engineering in multiple areas related to acquisition. It relies on implementation instances to further increase the guidance, support, and use of products out of digital engineering – digital artifacts. The DEWG, in concert with its internal exploration, retains a close tie to the industrial sector, for advice, advocacy, and for challenges in the areas of using the digital artifacts as cohesive elements across the government-industry boundary. The DEWG is also responsible for promoting and facilitating improvements in the expertise of the digital engineering stakeholders in the acquisition workforce.

Evidence across the Services and industry has affirmed digital engineering as a contemporary practice necessary to support acquisition in an environment of increasing global challenges, dynamic threat environments, and increasing life expectancy of our systems currently in operation. The DoD must continue to practice systems engineering efficiently and effectively to provide the best advantage for successful acquisitions and sustainment. Digital engineering updates the systems engineering practices to take advantage of computational technology, modeling, analytics, and data sciences.

ODASD(SE) is developing a Digital Engineering Strategy, which is built on five foundational elements necessary for a Digital Engineering Ecosystem to thrive:

  1. Formalize the development, integration, and use of models to inform enterprise and program decision making
  2. Provide an enduring authoritative source of truth
  3. Incorporate technological innovation to improve the engineering practice
  4. Establish a supporting infrastructure and environment to perform activities, collaborate and communicate across stakeholders
  5. Transform a culture and workforce that adopt and support digital engineering across the life cycle

It is anticipated that DoD Services and agencies, acquisition offices, industry, FFRDCs, and academia will develop their digital engineering implementations to ensure that the concepts of digital engineering are cast in a manner familiar to their stakeholder base.

Challenges and Goals

Defense programs are increasingly complex. Large systems and 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. The operational and threat environments are dynamic; and current practices are not keeping pace with technology and technique advancements.

To stay ahead of the demands for new and upgraded weapon systems, the Department of Defense must continually scrutinize its approach to acquisition and systems engineering, including its methods for use of models, simulation results, contemporary techniques, and tools 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 difficulty in cohesive use of models, and in collecting, managing, sharing, 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 and non-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.

Government and Industry are also challenged to train, maintain, and retain engineers and related stakeholders who understand and are able to implement, as well as develop, digital engineering practices. Assistance in this challenge area must come from DoD, Industry, tool vendors, and academia. As cohesion is a key element of digital engineering implementation, it is imperative that the different elements developed by those who can provide the knowledge not conflict, and if possible, that the elements be complementary.

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 digital information and computational capability 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 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 3, 3–2.4.2 Modeling and Simulation, 2017

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

Collaboration with Industry

DoD has teamed with the NDIA Systems Engineering Division/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).