The digital evolution of engineering reflects the transformation undergone by gaming itself. The way players previously entertained themselves with simple simulations, such as the Hunger Games Simulator, to try out complex scenarios has now been replaced by play engineers having us build digital systems on an entirely new level. The transition from analog to digital engineering is perhaps the biggest modern transformation in the industry.
Digital engineering is a game-changing methodology that’s transforming the way companies address complex engineering problems. While it’s obvious that businesses see the enormous value of incorporating digital solutions throughout their engineering processes (the global digital transformation market will reach an estimated $12.35 trillion by 2032, per Fortune Business Insights), how do they move from legacy to leading technologies?
What is Digital Engineering?
Digital engineering is a holistic approach that uses cutting-edge technology to create, maintain, and manage computer-based technical systems more efficiently. These approaches combine the system’s architecture, digital design, physics simulation, artificial intelligence (AI), and the concept of digital twin into a unified digital ecosystem.
The heart of this approach is breaking down traditional silos between departments, so that collaboration becomes second nature and real-time feedback is essential throughout the product lifecycle. The concept encompasses Model-Based Systems Engineering (MBSE), which utilizes dynamic models instead of traditional static documentation to describe and develop complex systems.
Key Components of Digital Engineering:
- System architecture and digital design inclusion
- Physics-based simulations and modeling
- AI and ML features
- Real-time surveillance through the use of digital twin technology
- Integrated design and engineering process across all aspects of the discipline
Simulation as a System
The development of simulation technology has evolved from the traditional approach of individual vehicle system models to tools that can be applied to each vehicle subsystem, and now to complex integrated systems-of-systems models. This transformation is representative of how we entertain, in a way that Google Block Breaker follows the classic arcade and user interactivity. Engineering simulation is increasingly the tie that binds disparate disciplines and tools into a single model that simulates complex product behavior.
Current simulation methods center on addressing the system-wide performance of cyberphysical products, as opposed to optimizing subsystems independently. This systems perspective enables engineers to understand how changes in one area of the product affect the rest of the product ecosystem, which leads to improved overall performance and fewer costly surprises that arise only during physical testing.
The Role of AI in Digital Engineering
AI transforms digital engineering in at least three aspects:
- AI for Prediction: Machine learning models review past data to predict future performance, giving engineers tools to identify issues early in the process before they end up locked into a physical prototype.
- AI for Generation: Enter a set of specifications and limitations, and today’s advanced AI models can develop new, high-performance designs, creating them in much less time than traditional methods.
- AI for Insights: AI extracts patterns from massive amounts of engineering data to deliver actionable insights that human analysts can overlook.
Robust data management is crucial for effectively ingesting and organizing training data within the context of AI in digital engineering workflows. This is where SPDM systems become pivotal.
The Importance of SPDM
Simulation Process and Data Management systems fill in a gap within the engineering workflow that was not meant to be performed by conventional Product Lifecycle Management (PLM) systems. SPDM solutions provide a centralized system for managing simulations, data, and workflows across an entire organization.
Benefits of SPDM Implementation:
| Benefit Category | Specific Advantages | Impact Metrics |
| Data Management | Centralized simulation storage, Searchable data archives | 40-60% reduction in data retrieval time |
| Workflow Optimization | Automated process capture, Repeatable analysis workflows | 30-50% faster simulation setup |
| Collaboration | Cross-disciplinary data sharing, Unified digital thread | 25-35% improvement in team productivity |
| Compliance | Audit trails, Version control, Traceability | 70-80% reduction in compliance preparation time |
| Cost Reduction | Reduced physical prototyping, Optimized resource allocation | 20-40% decrease in development costs |
SPDM solutions establish a digital thread that connects all engineering domains along the product lifecycle, promoting traceability and collaboration, while also retaining knowledge from project to project.
Overcoming Implementation Challenges
There are many hurdles that organizations need to overcome when they want to shift to a digital engineering method. The change is more than just about picking up a technology and starting to use it; it requires culture change management that takes into account people, processes, and the way organizations work.
Common Implementation Hurdles:
- Reluctance to shift from tradition, very physical-first methods
- Complexity of integration with the used engineering systems
- Deficiency in competencies for digital engineering approaches
- Initial Investment Fears and ROI Uncertainty
You need to show measurable results with KPIs like shorter cycle times, greater efficiency, faster time to market, and a decrease in quality risk. Companies that treat digital engineering as a journey rather than a step change tend to fare better in the long run.
The Future of Digital Manufacturing
Digital engineering is admitting the air for an industry revolution. The convergence of simulation-led design, AI-driven optimization, and real-time digital twins is leading to opportunities for “getting it right the first time” strategies in manufacturing.
Cloud computing is making advanced simulation capability available to all organizations, small and large alike, without the need for significant infrastructure investments in high-end engineering tools. And the computing burst capacity, made available on demand to carry out rapid complex simulations, will ensure cost-effective performance from your team.
Emerging Trends:
- Analysis as certification/assessment for regulatory compliance
- Digital twins-enabled autonomous process control
- Generative design optimization
- Real-time performance monitoring and adjustment