Digital technology is changing nearly every job, process, and industry around the world. According to Fortune Business Insights, the global digital transformation market was valued at $2.27 trillion in 2023 and is expected to grow to $12.35 trillion by 2032.
With the rise of technologies like artificial intelligence, machine learning, cloud computing, and simulation-based digital twins, it’s not surprising that more companies are using digital tools to improve their engineering processes.
So, what exactly is digital engineering? Why is it more important now than before? And how can businesses start using it? To answer these questions, Ansys spoke with several of its top simulation and industry experts to talk about how digital engineering has grown and how it is changing simulation, industry, and engineering overall.
The Growth of Digital Engineering
Q: How do you define digital engineering?
Scott Cunliffe, Manager of Application Engineering, New & Emerging Technology at Ansys:
Digital engineering is the use of modern technology to design, build, and manage engineering systems in a more efficient way. It applies to everything from small product designs to large industrial projects. It brings together system architecture, digital design, physics-based simulations, artificial intelligence, and digital twins, all linked by a digital thread that connects teams and helps them work better together. This allows engineers to fully capture design needs, testing, and approval steps in a virtual environment, which helps reduce the need for physical prototypes.
It also provides real-time feedback by using performance data from the actual physical product, helping improve the design and maintain its performance over time.
Q: How has digital engineering changed in the past ten years, and what role has simulation played?
Andy Ko, Director, Ansys Professional Services:
Over the last ten years, simulation has moved from being used as a separate tool to a more central part of the entire engineering process. Engineers used to focus on improving specific parts of a system, but now they look at the full picture. This bigger view is often called a “systems” or “system-of-systems” approach.
This change also brought about model-based systems engineering (MBSE), where systems are described through models instead of documents. These connected engineering models and simulations show how the whole system works. It’s like how we moved from hand-drawn blueprints to computer-aided design (CAD), and now we use those designs for simulations like finite element analysis (FEA) and computational fluid dynamics (CFD).
Now, simulation is a common part of engineering. The goal is to connect all these digital models so they work together and focus on the overall performance of the product, not just individual parts.
Scott:
One major improvement has been moving away from document-based methods to fully connected MBSE tools, where simulations are used to check how well a design will perform, including the hardware. As MBSE becomes more common, simulation tasks become more complex and require a group of software tools to manage everything.
To keep up, companies are using simulation process and data management (SPDM) systems. These systems store and manage simulation work that product lifecycle management (PLM) systems weren’t built for. With SPDM, complex simulations can be repeated and improved using multidisciplinary design analysis and optimization (MDAO) tools. These tools can test different versions of a design to find the best options. Capturing how these tools work together allows teams to use them on a larger scale across multiple teams.
As simulations and these toolchains get more complex, high-performance computing (HPC) is needed, and cloud computing now helps run these heavy tasks remotely. At the same time, artificial intelligence is becoming more common. It uses past data to predict results and even create new, better-performing designs.
Overcoming Barriers to Digital Engineering
Q: What strategies can help convince leaders to switch from physical to digital-first approaches?
Reni Raju, Senior Manager, Technical Account Management at Ansys:
To get leadership on board with using digital tools instead of physical prototypes, it’s important to connect the benefits to their main business goals. They want to see measurable improvements in things like faster development, lower costs, better efficiency, quicker time to market, and fewer risks with quality or warranties.
Marc Horner, Distinguished Engineer at Ansys:
In healthcare, companies usually depend on physical testing like bench and animal testing to ensure safety. These methods are well-established by each country’s regulations. However, because of market demands like speed, fewer recalls, and a shift toward results-based medicine, companies are under pressure to launch better, safer products faster. To meet this challenge, more companies are looking into digital tools, not just for design, but for the entire product lifecycle.
We’ve created examples showing how Ansys tools can support digital engineering. These examples are helping customers see what’s possible in the future, though many still see them as early-stage or experimental.
Q: What challenges do companies face when starting with digital engineering?
Marc:
In the medical device industry, many companies are still just exploring what digital engineering can offer. They’re hiring outside experts to help figure out how digital tools could make their processes better. One of their main goals is to get a clear view of where their current product design processes stand.
In the pharmaceutical field, businesses are investing in digital manufacturing to make their production more reliable. The long-term goal is to use digital twins to control manufacturing automatically, which would ensure both quality and efficiency.
Q: How can companies get the most value from simulation and digital tools?
Reni:
The main returns—like lower costs, faster development, and better efficiency—come from using simulation and digital engineering tools. When companies use virtual testing instead of only physical testing, they save time and money. Digital engineering also makes it possible to use more flexible and faster product development, which is a big help when dealing with complex designs. A model-based approach also allows companies to reuse past knowledge, cutting down on wasted effort.
The Efficiency Advantage of Digital Engineering
Q: What stops some companies from using digital engineering to its full potential?
Reni:
Companies are at different stages when it comes to using simulation and digital tools. Success doesn’t only depend on the technology—it also depends on people, processes, and company culture. Adopting digital engineering is not a one-time change, but a gradual shift toward becoming a model-based organization.
Historically, physical testing was the main way to check if a product worked well. But as designs get more complex, it takes more testing to make sure everything works as expected. Simulation helps handle this complexity. With strong validation processes, virtual models can predict how a system will perform.
Some industries are already working to meet regulatory needs with simulation, through methods like certification by analysis (CbA). More advanced model validation techniques are being put in place to build confidence in digital predictions. For now, digital engineering helps bridge the gap between physical and digital testing, improving collaboration and development. In the future, we may get to a point where “getting it right the first time” with digital tools becomes the norm.
SPDM tools like Ansys Minerva help manage simulation and data across a company through one central platform, forming a connected digital thread.
Q: What are the limits of traditional simulation, and how does digital engineering improve things?
Scott:
In the past, engineering teams worked separately. Mechanical, electrical, and other groups had little contact. MBSE and SPDM have changed that by giving everyone a shared system and language. The digital thread connects every step of the engineering process.
Simulation has also become more accurate, and with better computing power, engineers can now run more detailed models.
Andy:
Traditional simulations often focus on perfecting one part of a product. But what’s ideal for one part may not be best for the whole product. Most good products strike a balance among different parts. Traditional simulations are also mostly used by experts, and the data often isn’t shared or managed well.
Digital engineering changes this by connecting all the models in a structured way. It opens up the process, helps teams work together, and focuses on solving problems from a full-system point of view.
Having data and models stored in a shared, trusted space makes it easier for teams to find what they need and communicate better. It also saves time because engineers spend less effort searching for data.
The Bigger Picture of Digital Engineering
Q: What are the most exciting developments in simulation and digital engineering today?
Scott:
AI is driving a lot of progress in predictions, design generation, and gaining insights. But it all depends on managing the data well. SPDM plays a key role by organizing all engineering data for AI and other tools. Digital twins are another big step—they link the design and physical versions of a product, helping engineers work more closely. NVIDIA Omniverse also shows promise for helping teams visualize digital systems and reach a complete digital twin.
Q: How are these tools changing how we use simulation?
Scott:
People are starting to treat their simulation data more seriously because it will be used by others in the process. Even failed simulations are useful as training data for AI. All data has value now.
Q: How can generative design and simulation-driven methods change product development?
Andy:
These tools should help create better designs and give engineers more useful insights. They can also cut down development time by reducing the need for many physical prototypes. But we need to remember that engineering is just one part of product development. Business decisions also factor in, so improvements in engineering need to be seen as part of a larger process.
Q: How does cloud computing help with simulation and digital engineering?
Scott:
Cloud computing allows engineers to run simulations without needing expensive computers. This makes digital tools more accessible to all types of companies. It also helps teams manage cost and performance better. Using extra computing power when needed helps complete complex tasks faster.
Q: What skills do future engineers need for digital engineering?
Reni:
Engineers will need to think across different fields and work well with others. Ansys’s white paper talks about this, stressing the need for both broad knowledge and teamwork.
When people learn different skills, they can solve real-world problems more effectively. It also helps them find better ways to work. Teamwork helps people share ideas and learn from each other, which leads to more creative and productive teams.
Andy:
Engineers need to think about how their work fits into the bigger picture. In the past, they worked separately and focused on their own part. That’s changing. Now, engineering decisions affect the whole product, and engineers must consider the full system in their work.