… by Radha Krishnan, President and Founder, Detroit Engineered Products (DEP)
Computer Aided Engineering was originally introduced to answer fundamental engineering questions with greater confidence than physical testing alone could provide. In its early days, CAE helped engineers understand stress, deformation, vibration, and thermal behaviour before committing to costly prototypes. It reduced risk, improved safety, and shortened development cycles in industries where failure was not an option. For a long time, this role was sufficient. Simulation validated designs after key decisions were already made.
That traditional role is now under strain. High-volume manufacturing environments have changed the rules of product development. Companies no longer deal with a handful of products released every few years. They manage large product families, frequent refresh cycles, regional variants, and growing regulatory complexity. In this context, CAE is expected to support not dozens, but hundreds or thousands of design decisions. The question is no longer whether simulation is accurate enough. It is whether it can keep up.
The Limits of Traditional CAE Workflows
Traditional CAE workflows are deeply dependent on expert intervention. Geometry preparation, meshing, load definition, solver selection, and result interpretation all rely on individual judgment. This produces high-quality results, but it also introduces variability and delays. Each design change often triggers a full rework of the model. Each analysis becomes a custom exercise.
In high-volume manufacturing, this approach leads to excessive iteration loops. Designs are updated based on late feedback. Manufacturing constraints surface after tooling decisions. Engineering teams spend more time reacting than optimizing. As product volumes increase, the gap between simulation demand and CAE capacity widens.
The problem is not a lack of simulation tools or compute power. It is that CAE processes were never designed for repetition at scale.
Reducing Iterations by Changing When and How CAE Is Used
Reducing iterations does not mean running fewer simulations. It means running the right simulations earlier and embedding their results directly into decision making. When CAE is applied late in the process, even accurate results lead to rework. When it is applied early, it prevents weak concepts from progressing.
To move upstream, CAE must tolerate change. Early-stage designs are incomplete, evolving, and sometimes ambiguous. Traditional workflows struggle here because they demand detailed, finalized inputs. Scalable CAE accepts approximation initially and refines fidelity as designs mature.
Standardized assumptions, reusable templates, and parameter-driven models allow simulations to be updated quickly as geometry changes. Instead of restarting the process, engineers adjust inputs within a controlled framework. This approach dramatically shortens feedback loops and reduces unnecessary redesign cycles.
From Individual Expertise to Organizational Capability
Another key to scaling CAE is shifting from individual expertise to organizational capability. In many companies, the most valuable CAE knowledge exists in the minds of a few experienced analysts. Their judgment is critical, but it does not scale easily across teams, regions, or suppliers.
High-volume manufacturing requires consistency. Results must be comparable across programs and locations. This is only possible when methods are clearly defined, documented, and embedded into workflows. Best practices must be treated as assets, not personal preferences.
When CAE workflows are formalised, simulation becomes less dependent on who runs the analysis and more dependent on the process itself. This does not eliminate the need for experts. Instead, it frees them to focus on method development, complex problems, and continuous improvement rather than repetitive execution.
Automation As a Prerequisite for Scale
Automation is the practical mechanism that makes scalable CAE possible. Automated geometry handling, meshing, load application, solver execution, and post-processing reduce manual effort and eliminate many sources of inconsistency. More importantly, automation enforces discipline.
However, automation alone is not enough. Rigid scripts that fail when designs change create frustration rather than efficiency. What high-volume environments require is intelligent automation that understands design features, manufacturing intent, and analysis objectives.
At around this stage of CAE evolution, tools like DEP MeshWorks, ANSA, HyperMesh, Simcenter, Abaqus with advanced scripting, and MSC Apex are reshaping the landscape by combining automation with AI-driven decision support, enabling repeatable, scalable simulation with far less manual intervention.
This shift allows simulation to become part of everyday engineering workflows rather than a specialized, time-consuming activity.
Using Data to Guide Simulation Effort
As CAE scales, so does the amount of simulation data generated. Every analysis contains insight, but only if it is captured and reused. Historically, simulation data was archived and forgotten once a project ended. In high-volume manufacturing, this represents a missed opportunity.
Data-driven CAE uses historical results to inform future decisions. Patterns emerge across variants, load cases, and materials. Machine learning models can identify sensitivities and predict performance trends. This allows teams to focus high-fidelity simulation where it matters most and rely on faster screening methods elsewhere.
By prioritizing simulation effort intelligently, organizations reduce unnecessary iterations while maintaining confidence in critical decisions. This balance is essential when supporting large product portfolios under tight timelines.
Aligning CAE With Manufacturing Reality
Scaling CAE also requires stronger alignment with manufacturing processes. In high-volume production, small variations in material properties, forming processes, or assembly conditions can have large downstream effects. Simulation that ignores these realities risks losing credibility on the factory floor.
Modern CAE workflows increasingly incorporate manufacturing effects such as residual stresses, distortion, and tolerance variation. When these effects are modeled systematically, simulation results become more predictive and more trusted.
Equally important is feedback from production and quality teams. Field data, warranty issues, and inspection results should inform simulation assumptions. This closed-loop approach turns CAE into a living system that improves over time.
Organizational Commitment and Cultural Change
Technology alone does not scale CAE. Leadership commitment is critical. Organizations must position CAE as a core decision-making capability, not a support function called in after problems arise. This requires investment in methodology development, automation infrastructure, and training.
It also requires cultural change. Design and manufacturing engineers need to trust simulation outputs and use them proactively. Success metrics should reward early issue prevention rather than late-stage heroics. When CAE is embedded into daily engineering decisions, its value multiplies.
Conclusion
Scaling CAE for high-volume manufacturing environments is ultimately about transformation. It is about moving from isolated analyses to integrated systems, from individual expertise to shared capability, and from reactive validation to proactive decision support. By reducing iterations, moving CAE upstream, embracing intelligent automation, and aligning simulation with manufacturing reality, organizations can unlock the full potential of CAE.
In a world where product complexity and volume continue to rise, scalable CAE is no longer a competitive advantage reserved for a few leaders. It is a necessity for any organization that aims to deliver quality, cost, and speed at scale.
