The Ultimate Guide to Digital Engineering

What Is Digital Engineering

The term "digital engineering" has gained prominence in the technical communities. This article will cover the definition, core concepts, challenges, and best practices. 

Digital Engineering Definition 

Defense Acquisition University offers a clean definition of digital engineering. 

“A means of using and integrating digital models and the underlying data to support the development, test, evaluation, and sustainment of a system.” -  DAU Glossary

Digital engineering leverages computer models and digital information systems as a continuum from concept through disposal. In short, it replaces traditional document-based processes with data integration, model-based representations, and collaboration across teams.

 Digital engineers leverage:

• digital artifacts as the source of truth for performance
• change management
• informed decision-making

Digital engineers' goals are:

• Deliver high-payoff solutions to the warfighter at the “speed of relevance.”
• Connect people, processes, data, and capabilities with a digital enterprise.
• Improve technical, contract, and business practices through an authoritative source of truth and digital artifacts.

History and Evolution of Traditional Engineering

Computers, digital tools, and systems engineering practices paved the way for the digital engineering we know today.

Early Foundations

Early systems engineering methods developed at Bell Labs in the 1940s aided in complex projects, such as the Manhattan Project. They used formal analysis techniques that foreshadowed digital methods. With the invention of microchips, semiconductors, and early computers in the 1950s, engineers could utilize the digitization of manual processes and complex calculations in engineering.

Computer-Aided Design and Simulation

The late 20th century witnessed the introduction of computer-aided design (CAD) systems, which enabled engineers to create, modify, and optimize designs digitally, marking the origins of modern digital engineering tools. As computational power grew, digital models became central to engineering practice, gradually replacing drawings and physical prototypes.

Rise of Digital Transformation

Digital engineering accelerated during the internet era (1990s onward), enabling global collaboration, data sharing, and integrated digital workflows. The mobile era (2007–2019) and the proliferation of cloud computing brought about even greater connectivity, enabling teams to work and innovate remotely. These recent innovations made the digital transformation we see today possible.

Recent Advances and Today’s Landscape

The adoption of model-based systems engineering (MBSE), digital twins, and simulation-based design in the 21st century expanded digital engineering’s scope from product visualization to lifecycle management, optimization, and predictive analytics.

Cloud infrastructure, generative AI, and digital threads now enable seamless integration and real-time collaboration, making digital engineering a central pillar in industries such as aerospace, automotive, and energy.

Why Digital Engineering Matters

Digital Engineering is constantly evolving with new technology and ways of working, tackling issues such as data privacy and the need for skilled workers. Today, digital engineering is vital because it makes things more efficient, saves money, and encourages new ideas, giving businesses a significant edge in a fast-paced tech world.

Efficiency Gains

Digital engineering streamlines operations by enabling virtual simulations, digital twins, and automation, which eliminate redundancies and speed up design cycles. Organizations report up to 25% increases in productivity through the use of model-based workflows, digital collaboration platforms, and real-time data analysis. Idaho National Laboratory is successfully utilizing digital engineering by creating a digital ecosystem that incorporates tools such as Innoslate, Windchill, Autodesk, and Creo.

Cost Reduction

Another reason digital engineering is gaining momentum so rapidly is due to cost reduction. Companies are reporting up to 20% reductions in development costs through leveraging digital engineering tools and processes, such as virtual prototyping, simulation, and predictive modeling.

•  Virtual Prototyping:  Minimizes expenses associated with trial and error and material waste from physical models. 
• Predictive Maintenance: IoT sensors and digital twins enable us to anticipate equipment failures and prevent costly downtime.

Innovation Enablement

Digital engineering accelerates innovation by enabling the rapid testing of new ideas, designs, and processes virtually, thereby reducing barriers to experimentation. Data-driven insights facilitate better decision-making, enabling businesses to introduce products tailored to market needs and improve customer experiences.

AI, IoT, and simulation technologies embedded in digital engineering empower teams to design more innovative, safer solutions, from autonomous vehicles to healthcare devices.

Competitive and Strategic Value

Businesses that adopt digital engineering can scale and adapt quickly to evolving market demands, enhance collaboration, and stay ahead of competitors through continuous process optimization and innovation.

Enhanced cybersecurity and sustainability practices are supported by digital engineering, aligning enterprise operations with modern regulatory and environmental standards.

Digital engineering’s integration of advanced technologies, data analytics, and collaborative digital workflows makes it indispensable for organizations seeking efficiency, cost-effectiveness, and continuous innovation in today's competitive environment.

3 Core Concepts of Digital Engineering

Digital Engineering comprises three key concepts: authoritative source of truth, model-based systems engineering, and modeling and simulation.

1. Authoritative Source of Truth

A critical principle in digital engineering is establishing a “single source of truth” or authoritative, model-driven repository that manages system data and supports informed decision-making throughout the lifecycle.

2. Model-Based Systems Engineering

Digital engineering is a broader discipline that encompasses Model-Based Systems Engineering (MBSE), which focuses explicitly on utilizing digital models for system design and simulation, rather than relying solely on documents.

Digital engineering transforms legacy engineering practices by integrating digital technologies, model-based data, and cultural change to drive better business outcomes and reduce risk across large-scale, complex projects.

3. Modeling and Simulation

Modeling and simulation are central to digital engineering, enabling engineers to create, analyze, and validate digital representations of complex systems before building physical prototypes. This reduces development costs and time, increases design confidence, and improves product quality.

Simulation workflows managed within Model-Based Systems Engineering (MBSE) environments, allow multidisciplinary teams to collaborate, optimize trade studies, and verify system behaviors across the life cycle. High-fidelity models, combined with cloud-based computing resources and AI-driven analysis, further enhance simulation accuracy and scalability, supporting ongoing digital transformation in engineering practice.

Data-Driven Engineering Principles

Data-driven engineering leverages advanced modeling, simulation, and analytics to transform the way complex systems are conceived, built, and maintained.

By integrating capabilities such as digital twins, virtual prototypes, and interoperable engineering frameworks, organizations can establish a continuous feedback loop between the physical and digital worlds, enabling smarter decisions, faster innovation, and improved lifecycle performance.

Digital Twins and Virtual Prototypes

Digital twins are dynamic, virtual representations of real-world systems, capturing everything from geometry to behavior, and are often updated in real-time via sensor data.

These enable continuous simulation, monitoring, and optimization across the entire life cycle, from conceptual design to decommissioning. 

Virtual prototypes allow engineers to simulate, test, and refine designs in silico, minimizing the need for costly and time-consuming physical tests.

Digital twins enable bidirectional feedback between physical and digital spaces, facilitating predictive maintenance, performance optimization, and agile response to operational changes; key tenets of digital engineering.

Digital Materiel Management

Digital materiel management is seen as a philosophy or guiding principle that underpins how organizations, such as the Air Force, approach acquisition, research, operations, and sustainment activities.

It involves ingraining digital-first thinking and model-based practices across all lifecycle processes to achieve better decision-making, agility, and data-driven management.

Digital Product Engineering

Digital product engineering is dedicated to bringing new software products to market through iterative, user-centric approaches.

Digital engineering is chosen for enterprise-wide modernization or data/AI initiatives.

Digital product engineering is selected when organizations want to launch, scale, or enhance digital offerings for customers.

Systems Integration and Interoperability

Digital engineering depends on seamless integration and interoperability across tools, models, and data sources. This is achieved via frameworks and technologies, including semantic web and ontologies, that align disparate engineering artifacts into a unified, tool-agnostic digital thread. Such approaches enable authoritative sources of truth, continuous traceability, and automated reasoning across multi-domain systems.

Integration and open interoperability accelerate cross-disciplinary collaboration, avoid data silos, and ensure that digital engineering environments can support the growing complexity of modern engineered systems.

Concept Development and Design

This initial stage focuses on defining the system’s goals, constraints, and high-level architecture using digital models and collaboration tools, ensuring that all stakeholders share a unified vision before detailed development begins.

Verification and Validation in a Digital Environment

System functions and requirements are continuously checked and validated using automated, model-driven test cases and simulation scenarios, enabling the early detection and correction of defects entirely within the digital domain.

Deployment and Maintenance with Digital Tools

Once validated, designs are transitioned (deployed) into production using integrated digital platforms. At the same time, operational systems are supported with digital twins, monitoring tools, and connected maintenance frameworks to ensure high availability and continuous support.

Feedback Loops and Continuous Improvement

Operational data and user feedback are fed back into models through digital tools, enabling ongoing refinement, optimization, and rapid iteration across the lifecycle, allowing for an agile response to changing requirements and environments.

These steps, taken together, streamline the engineering process, drive collaboration, and maintain a continuous digital thread from concept to operation.

Digital Engineering of a Lunar Rover

Learn how to build a digital twin by integrating Ansys STK, AGI, GitHub, MATLAB, Raspberry Pi, and Sopatra, using Innoslate as your authoritative source of truth.

Tools and Technologies

Overview of Leading Digital Engineering Software

To create a digital engineering ecosystem, you need a suite of tools, including Model-Based Systems Engineering (MBSE) simulation and modeling.

• Innoslate offers a browser-based, cloud-oriented platform supporting the entire systems engineering lifecycle, with compliance to key industry standards and integrated simulation, requirements, and risk analysis.
• CAMEO Systems Modeler (part of Dassault Systèmes) excels in handling large, complex SysML-based models, offering strong support for collaboration and integrations.
• Sparx Systems Enterprise Architect is recognized for its versatility, broad modeling capabilities, and cost-effective deployment for large teams.
• IBM Rhapsody focuses on embedded and real-time system development, ideal for software-centric systems engineering.
• Capella is an open-source alternative based on the Arcadia method, emphasizing architecture-centric modeling with traceability, collaboration, and extensibility across various industries, including aerospace and defense.

Each tool varies in support for standards, integrations, model scale, and collaborative features.

Cloud Computing and Cloud Storage Considerations

Cloud computing is now fundamental for digital engineering environments, offering:

• Agility and Flexibility for rapid model experimentation and modernization.
• Scalability to run large-scale simulations and handle extensive engineering data, leveraging massive compute and storage resources as needs fluctuate.
• Collaboration enables distributed teams to work securely and efficiently.
• Reliability and Security, supporting compliance with strict industry or defense requirements.
• Cost-effectiveness due to the pay-as-you-go model.
• Massive data sets and simulation workloads are easily managed with cloud services, while global connectivity supports enterprise-wide collaboration and democratized data access.

AI and Analytics in Digital Engineering

Artificial intelligence and advanced analytics drive digital engineering transformation:

• AI and generative analytics automate tasks, analyze complex data, and power advanced applications like digital twins, predictive maintenance, and design optimization.
• Machine learning is increasingly used for simulation acceleration, requirements analysis, risk forecasting, and real-time operational feedback.
• AI-powered tools enhance efficiency, accelerate innovation cycles, and enable smarter, continuous system improvement across various domains, including product design, smart manufacturing, and customer support.

The integration of AI and analytics with MBSE platforms and cloud infrastructure accelerates digital transformation, delivering actionable insights throughout the engineering lifecycle.

Digital Engineering Examples by Industry

Digital engineering is transforming multiple industries by enabling data-driven, model-based approaches that streamline design, production, and operation. Here's an overview of its application across key sectors:

• Aerospace and defense
• Automotive and transportation
• Healthcare and medical devices
• Manufacturing and industrial systems
• Infrastructure and smart cities

Aerospace and Defense

Digital engineering accelerates the development of advanced systems (like aircraft and spacecraft) by using digital twins, simulation, and integrated data threads to optimize design, reduce time-to-market, and minimize costs.

Technologies such as AI, additive manufacturing, and IoT enable predictive maintenance, real-time performance validation, and agile collaboration across complex supply chains.

Defense agencies rely on digital engineering to modernize, prioritize rapid delivery, and enhance system resilience.

Automotive and Transportation

In the automotive industry, digital engineering drives smarter, software-defined vehicles that utilize AI for autonomous driving, edge computing for real-time data processing, and digital twins for virtual testing and validation.

Predictive maintenance, smart manufacturing, and connected systems make vehicles safer, more reliable, and adaptable to emerging trends in the mobility sector. Industry leaders utilize these technologies to integrate design, production, and supply chain operations, ensuring efficiency and innovation.

Healthcare and Medical Devices

Digital engineering enables the full integration of medical devices within data-rich healthcare platforms. AI-powered systems support diagnostics and monitoring, while digital twins and robotics improve precision and outcomes in surgery and treatment.

Devices now collect real-time patient data, enabling personalized care, predictive analytics, and comprehensive healthcare management throughout the patient journey, from diagnosis to post-treatment monitoring.

Manufacturing and Industrial Systems

Manufacturers use digital engineering for virtual prototyping, real-time data monitoring, and predictive analytics.

IoT sensors and AI drive proactive maintenance, reduce downtime, and streamline production.

Simulation and digital twins enable engineers to test, optimize, and refine equipment and workflows, thereby reducing waste and enhancing product quality. These practices improve agility, safety, profitability, and sustainability across industrial operations.

Infrastructure and Smart Cities

Digital engineering powers smart cities and modern infrastructure by integrating IoT, AI, and automation with urban systems.

Real-time data from sensors enables the management of traffic, utilities, energy, waste, and public safety.

Engineers design infrastructure to be connected, responsive, and sustainable—with applications ranging from intelligent traffic management and smart grid energy optimization to predictive maintenance of public assets.

Collaboration across sectors ensures scalable, secure, and adaptable urban solutions.

Trends and Future of Digital Engineering

Digital engineering is rapidly evolving with significant future trends across several key factors:

AI-Driven Design and Predictive Analytics

AI and machine learning are revolutionizing engineering design by enabling predictive analytics that analyze vast data sets from simulations, testing, and operations. This capability enables engineers to proactively optimize designs, identify potential failures, and expedite development cycles by minimizing trial-and-error.

AI-powered predictive design enhances productivity, resource allocation, and risk mitigation, making engineering workflows more agile and data-informed.

Increased Adoption of Digital Twins

Digital twins are becoming ubiquitous as businesses recognize their value in monitoring, simulating, and optimizing physical assets in near real-time. Future digital twins will be more intelligent and more autonomous, leveraging AI to self-update and adapt with minimal human intervention. 

Trends such as Twin-as-a-Service (TaaS), human digital avatars, and domain-specific twins (e.g., genomic twins in healthcare) underscore the expanding scope and business implications of these virtual counterparts. The digital twin market is expected to grow substantially, driving operational efficiency, sustainability, and innovation across industries.

Open Systems and Collaborative Engineering Environments

The future of digital engineering depends on open ecosystems that integrate diverse tools, data standards, and workflows. Open systems support interoperability, reduce redundant efforts, and enable seamless collaboration between cross-disciplinary teams and organizational boundaries.

This flexibility ensures digital engineering environments can evolve with changing technologies, comply with standards, and incorporate legacy systems while fostering innovation. Open, modular architectures are fundamental for scaling digital engineering across complex system lifecycles.

Integration with IoT and Cyber-Physical Systems

Integration with IoT and cyber-physical systems (CPS) is critical to digital engineering’s future, enabling real-time data capture and bidirectional feedback between physical assets and their digital counterparts. IoT sensors provide continuous streams of operational data, enabling digital models to accurately reflect current conditions and facilitating predictive maintenance, automated control, and enhanced resilience. 

The convergence of IoT, CPS, and digital twins results in more intelligent and autonomous systems in manufacturing, infrastructure, transportation, and other sectors. However, challenges remain around interoperability, security, and data management as these technologies scale.

Together, these trends position digital engineering as a cornerstone of next-generation systems development—delivering smarter, faster, and more connected engineering processes that drive innovation and operational excellence.

Digital Engineering Challenges and Best Practices

Below is a list of the most common digital engineering challenges that engineers and managers face when adopting a digital ecosystem. 

• Common implementation challenges (cultural, technical, and resource)
• Data management and governance
• Change management and adoption strategies
• Ensuring cybersecurity in digital engineering systems

Common Implementation Challenges (Cultural, Technical, Resource)

Cultural resistance remains a significant barrier as digital engineering transforms traditional workflows and roles. Leadership misalignment, workforce skill gaps, and insufficient training contribute to the reluctance to adopt new technologies. Technically, integration complexities, legacy systems, and immature tools can hinder progress. 

Resource constraints, including budget limitations and a lack of dedicated digital engineering talent, also limit successful deployment.

Best practices encompass clear communication of value, leadership alignment, targeted skill development, and iterative implementation strategies that address both social and technical dimensions holistically.

Data Management and Governance

Creating a data management plan is essential for digital engineering success, as it ensures data quality, accessibility, security, and compliance.

Robust governance frameworks define roles, policies, and standards controlling the data lifecycle from acquisition through storage, sharing, and disposal. 

Key elements include metadata management, data stewardship, quality assurance, and compliance with regulations (e.g., the General Data Protection Regulation, or GDPR).

Establishing trusted data pipelines and ownership enables confident decision-making and supports innovation. Continuous monitoring and policy enforcement safeguard data integrity, maximizing business value from engineering data.

Change Management and Adoption Strategies

Sustainable adoption hinges on people-focused change management strategies emphasizing user engagement, clear communication, and tailored training.

Avoiding overly rigid or superficial approaches, organizations should create dialogue spaces to understand end-user concerns, clarify benefits, and provide ongoing support. 

Early involvement of stakeholders, aligning change initiatives with daily workflows, and establishing governance around emerging technologies enhances buy-in.

Ethical and educational frameworks surrounding tools like AI prevent misuse and promote responsible usage, further smoothing the adoption pathways.

Ensuring Cybersecurity in Digital Engineering Systems

Cybersecurity is critical as digital engineering environments connect distributed teams, sensitive data, and a diverse array of tools.

Best practices include conducting continuous security posture assessments, enforcing role-based access controls, encrypting data in transit and at rest, and utilizing secure collaboration platforms. 

Monitoring for anomalies and establishing incident response plans enhances resilience. Integrating cybersecurity controls into digital engineering models (e.g., digital threads populated with cyber metrics) improves traceability, threat detection, and compliance speed.

Training engineering teams on cyber risks and updating software with patches further protects assets and intellectual property.

Addressing these challenges through thoughtful governance, cultural change initiatives, robust data practices, and effective cybersecurity strategies enables organizations to fully realize the benefits of digital engineering.

How to Implement Digital Engineering

First Steps on How to Get Started

1. Assess Your Current Capabilities and Needs

Begin by thoroughly evaluating your current systems engineering process, tools, and the skills of your team. SPEC Innovations provides gap analysis services to identify opportunities for digital transformation that align with your objectives.

2. Develop a Digital Engineering Roadmap

Work with our consultants to develop a step-by-step implementation plan, focusing on quick wins such as automating requirements and integrating simulation, while scaling towards enterprise-wide adoption.

3. Adopt a Cloud-Based MBSE Solution for Authoritative Truth

Experience rapid deployment of Innoslate’s unified platform, combining requirements management, simulation, and risk analysis in the cloud. Its user-friendly interface accelerates collaboration among dispersed teams.

4. Engage in Training and Continuous Improvement

Participate in SPEC Innovations’ training workshops and webinars, designed to enhance proficiency and cultivate a culture of digital systems engineering excellence.

5. Leverage Insights & Strategy for Growth

Stay ahead by subscribing to SPEC Innovations’ insights on MBSE market trends, best practices, and emerging technologies to refine your digital engineering capabilities continuously.

Resources to Explore

• SPEC Innovations Digital Engineering Services
• Innoslate Digital Engineering Overview PDF
• Digital Engineering Platform Approach for Missile Defense

Contact Us

Ready to transform your engineering process? Contact SPEC Innovations today to schedule a consultation or demo.