When “Perfect” Isn’t Practical: Why Great Design Depends on Understanding Imperfection
Published: November 7, 2025
Last Updated: November 7, 2025
There’s a quiet truth that every experienced engineer learns sooner or later: the real world doesn’t care how perfect your model is.
It begins with confidence - you’ve built your analytical model, verified the math, and the results make sense. Everything checks out. On paper, it should work. But then, you build the prototype, take it into the lab, and the data doesn’t line up. The observed and expected response don’t match. The behavior isn’t what you expected.
It’s a moment that can shake even the most seasoned engineer. What went wrong? Was the data bad? Was the model too simple? Did something in the setup go unnoticed?
According to Peter of FPrin, this isn’t failure, it’s the start of discovery. When analytical models and empirical data disagree, it’s tempting to toss the model aside and chase the problem experimentally, but that can lead to a painful cycle of trial and error and missed insights. Instead, Peter urges engineers to pause, reflect, and interrogate the assumptions behind both model and measurement.
Maybe there’s an implicit assumption hiding in the background, something so obvious it wasn’t even written down. Maybe a subtle variable, like room temperature or humidity, is distorting the data. Or maybe the test isn’t measuring quite what you think it is.
Peter describes the process almost like detective work: debug the system piece by piece, review the data collection method, seek a colleague’s second opinion, and always check whether your results fall within a realistic bounding case - a sanity check for what’s physically possible. If results fall outside that range, something more basic is off, perhaps a conversion error, a missed loss term, or a flawed understanding of the system itself.
And through that process, something important happens: you learn. The model evolves. The experiment becomes smarter. The engineer grows.
“All models are imperfect,” Peter reminds us, “but all models teach us something” — an echo of the classic insight from statistician George Box, who wrote that “all models are wrong, but some are useful”.
When Perfection Misleads
The same lesson applies in the digital world of CAD.
Modern MCAD tools are astonishing - we can now create detailed, intricate models faster and more precisely than ever. Parts fit flawlessly, surfaces are perfectly aligned, and assemblies come together like clockwork. But there’s a danger in that perfection.
Peter warns that perfect CAD models can be deceiving. In CAD space, everything is ideal - flat surfaces stay flat, holes align exactly, and materials never deform. But once those designs are built in the physical world, materials flex, parts expand, bolts creep, and structures shift ever so slightly under load.
This doesn’t mean CAD is flawed, it means that as engineers, we must look beyond geometry. FPrin’s team embraces a model-based design approach, using analytical models to simulate not only the ideal behavior, but also the imperfect reality: how systems respond to small variations in inputs, manufacturing tolerances, or use cases.
The goal, as he puts it, is to design systems that “don’t care” about variation - at least not within reason.
That means asking hard questions early:
If a component bends 50 microns under load, does it matter? If you’re measuring 20 microns, it does. If you’re measuring millimeters, probably not.
If a material creeps slightly over time, will that affect performance? For a flexible bracket, maybe not - but for a pressure fixture, definitely.
Understanding where precision matters and where it doesn’t is the difference between a functional system and a fragile one. It’s also what separates great engineers from those still chasing perfection that doesn’t exist.
As Peter notes, sometimes it’s even wise to over-design - not because you’re unsure, but because it buys you the freedom to explore “what-if” scenarios without breaking the experiment. The test fixture, after all, is just a means to an end: learning about how your system behaves under reality’s imperfect conditions.
The Fine Line Between Accuracy and Precision
This brings us to one of engineering’s most misunderstood pairs: accuracy and precision.
Accuracy tells you how close you are to the true value. Precision tells you how consistent you are even if you’re consistently off.
An analytical model might be precise, it’ll give the same result every time, but not necessarily accurate, especially if one assumption is off. Conversely, an experiment might be accurate once, but if it can’t produce the same result again, it’s not precise.
Both matter. Without precision, we can’t trust accuracy. And without accuracy, precision alone just means we’re repeating the same mistake efficiently.
Peter reminds us that analytical models produce perfectly repeatable outputs - same inputs, same results. But physical systems? Never quite identical. Even under “identical” conditions, small variations sneak in. That’s why replication - doing the same thing multiple times and seeing if results hold is so central to real engineering rigor.
Knowing When to “Do the DFM”
Another subtle trap many engineers fall into is treating Design for Manufacturability (DFM) as an afterthought - something to worry about after the design “works.”
But as Peter explains, manufacturability, reliability, usability, cost, and assembly - all those “DFx” principles are deeply interconnected. You can’t truly separate them. If a product works in theory but can’t be built, assembled, or maintained, it’s still a failed design.
He argues that the best design processes weave manufacturability and function together from the very beginning.
This mindset extends to how design engineers collaborate. When you ask a machinist, “Can you make this?”, the first question they’ll ask is, “What’s the material?” That answer isn’t trivial - it connects to stiffness, strength, machinability, thermal behavior, etc., all of which circle back to the model itself.
In companies like Toyota, the “Chief Engineer” role exists to balance all these priorities - a technical leader who understands enough about every “DFx” to mediate trade-offs between them. Most of us operate on smaller scales, but the lesson applies universally: effective engineering requires seeing the system as a whole, not a series of isolated checkboxes.
Precision in Context
Vitalii from Dystlab echoes this sentiment but adds an important nuance - context matters.
In construction, a 0.01 mm tolerance is absurdly tight. In energy or aerospace, it might be the bare minimum. Standards vary not only between industries but between countries and even engineering teams.
That’s why Dystlab performs all calculations at double precision - 16 digits after the decimal - ensuring it can meet the needs of the most demanding engineers. But not every project requires that level of fidelity. Sometimes, it’s enough to round off, provided you understand where those limits lie.
As Vitalii puts it, it’s about satisfying the most demanding user - but knowing when not to over-engineer for those who don’t need that level of exactness.
Vitalii from Dystlab echoes this sentiment but adds an important nuance — context matters.
In construction, a 0.01 mm tolerance might be absurdly strict. In energy or aerospace, it could be the bare minimum. Standards vary not only between industries but even between countries and engineering teams.
That’s why Dystlab’s own software, TechEditor, performs all calculations using double-precision arithmetic — roughly 16 digits of computational precision — ensuring it can meet the needs of engineers doing highly detailed analytical work.
But as Peter points out, there’s an important distinction here: computational precision is not the same as measurement precision.
Carrying a calculation to sixteen decimal places doesn’t make your result more accurate if your measurement tool only resolves one. If a diameter is measured as 10 mm ± 0.5 mm, then calculating its area with fifteen digits of π doesn’t change the fact that you can only meaningfully report it as about 79 mm².
In other words, overstating precision can be just as misleading as ignoring it altogether. The key is to match your level of detail to what your measurements — and your model — can genuinely support.
The Real Lesson
When models fail, prototypes misbehave, or data refuses to align, the easy path is frustration. The better path is curiosity.
Because that’s where the real work and real learning happens.
Engineering isn’t about achieving perfection. It’s about understanding what “good enough” really means, and having the judgment to know when good enough is actually great.
It’s about making peace with variation, using models to inform the design, and never forgetting that just because your system is working now doesn’t mean it will still work with the next batch of parts. A model that examines how performance responds to variation gives confidence that the design is robust to changes in input.
