Introduction
"We will build it ourselves." For many simulation teams, it sounds like the smart, agile, budget-friendly thing to do. But in reality, homegrown datasets come with a price (and not just in time).
Under the surface, DIY simulation data can introduce delays, create technical debt, and hold back your engineering momentum. Let's unpack why.
The illusion of control
"We will just build it internally": the default mindset
Most engineering teams are used to solving problems in-house. It is part of the culture. So when a new simulation need arises, the instinct is often: let’s measure a few things, extract the curves, and build the dataset ourselves.
At first glance, this approach feels efficient. It gives the team control. It avoids procurement friction. And it makes you feel resourceful.
Why it feels efficient (but it's not)
In practice, building a usable dataset is a project in itself. It means scheduling test sessions, cleaning and formatting data, tuning model parameters, and crucially validating results against physical behaviors.
Most of this work is invisible in early planning. The effort adds up later, in the form of bugs, rework, misalignment, and team overhead.
What gets overlooked
- Time: Building a dataset from scratch can take days if not weeks of senior engineers time.
- Quality: Without hybrid corrections and historical benchmarks, the data is only as good as the test setup.
- Traceability: Homemade files rarely come with the documentation needed to explain them six months later.
The real cost of DIY simulation
Engineer hours spent crafting instead of designing
Every hour spent debugging model inputs is an hour not spent on architecture, tuning, or trade-off decisions. And these hours are often invisible in your Gantt chart.
We explored this mismatch between effort and value in our article on simulation ROI.
Bugs, rework, and integration delays
Most datasets are not used by the person who built them. So when something breaks in CarMaker or VI-CRT, the original intent is lost and teams lose time chasing parameters that no one really owns.
Worse: when datasets are re-used across projects without standardization, every integration becomes a risk.
Poor documentation = technical debt
Homemade datasets are rarely versioned, structured, or traceable. Which means they do not scale. Engineers leave, projects pivot, teams reorganize and you are left with files no one understands.
When homemade becomes a blocker
Difficult to scale, reuse, or share
A dataset that works for one specific project with one simulator, one configuration, one user might not work anywhere else. Especially when the assumptions behind it are not recorded.
Simulation tools evolve. Teams change. But homemade datasets often lock you into a legacy setup.
Impossible to benchmark or compare options
One of the key strengths of simulation is the ability to test and compare. But that only works if your data allows for consistency and repeatability. Homegrown files, by definition, do not.
In contrast, simulation-ready datasets like those provided by MICHELIN SIMIX are built to be comparable across scenarios, tools, and components.
Risk exposure with clients and authorities
When your dataset becomes part of a submission file, a supplier handover, or a regulatory approval process, the stakes change. Homemade files often do not meet the robustness or transparency needed for external confidence.
One common pushback: "simulation-ready sounds like a one-size-fits-all." It's not. Think of it as tailored off-the-rack, built on a solid, validated foundation that covers most scenarios out of the box, and fully adjustable for the edge cases that are genuinely specific to your project. The time you save upfront is exactly what you reinvest in targeted fine-tuning. Unlike a homemade dataset, where most of your effort goes into getting the basics to run at all.
What professional datasets unlock
Immediate integration, pre-validated data
Simulation-ready datasets aren’t just files. They are data + context + validation. They come documented, versioned, and tuned for real-world conditions. You can plug them into your simulation tools with confidence.
Curious what that includes? Read our breakdown: what's actually in a dataset.
Team efficiency, alignment across projects
When your team works from the same dataset baseline, you reduce noise. ADAS, handling, NVH, durability all teams speak the same language. That means faster iteration, fewer surprises, and a clear audit trail.
This makes NVH optimization more predictive and less reactive.
This is one of the ways mature teams bridge the simulation vs reality gap, explained in detail here.
Cost reduction through reuse and reliability
Validated datasets can be reused, adapted, and extended, without reinventing the wheel each time. That is where the real savings kick in. Not just in hours, but in fewer errors, fewer loops, and more confidence at each step.
Conclusion: stop crafting, start engineering
You would not build your own CFD solver. Why are you still building your own datasets?
The real job of simulation engineers is not data cleaning. It is system understanding, decision-making, and collaboration.
So the next time someone says “let’s just build it ourselves,” ask: how much is that really costing you?
