Latest / Hardware Startups with Fexingo: Devices, Robotics, and Manufacturing Tech Companies / How Hardware Startups Are Using Digital Twins to Slash Iteration Costs
Transcript
- Lucas: There's this drone startup I've been following—let's call them AeroVane, not their real name—and they were burning through cash on physical prototypes. Each iteration cost them around twelve thousand dollars. Machining, PCB respins, labor. After five rounds, they had sixty grand in the hole and still hadn't solved a basic stability issue in gusty wind. Luna: Ouch. And you don't get that money back. So what'd they do? Lucas: They switched to a digital twin workflow. Built a full simulation of the drone in Ansys Twin Builder—modeled the aerodynamics, the motor response, the IMU noise. Then they ran two hundred virtual iterations in about a week. The final physical prototype worked on the first flight. Luna: Two hundred iterations for the cost of maybe one physical prototype. That's the whole thesis behind digital twins for hardware startups. Lucas: Exactly. And it's not just drones. Digital twins are becoming a must-have for any hardware startup that faces complex physics—which is most of them. The concept has been around since NASA used it for Apollo, but the tools have finally become affordable and accessible. Luna: Let's define it for listeners who haven't run into the term. A digital twin is a virtual model of a physical product that's updated with real-world data. It mirrors the product's behavior throughout its lifecycle. Lucas: Right. For a hardware startup, the most immediate use case is during development. Before you cut metal or spin a PCB, you simulate how the device will behave. You catch thermal issues, vibration modes, electromagnetic interference—all in software. The cost of fixing a problem in simulation is essentially zero. Fixing it after you've built a hundred units can be catastrophic. Luna: And the simulation model doesn't have to be perfect. It just has to be good enough to catch the big failure modes. You can refine it as you go. Lucas: That's a key point. A digital twin is a living model. You start with a rough cad based simulation, then feed in data from early prototypes—vibration test results, power draw measurements—to improve its accuracy. By the time you're ready for manufacturing, your digital twin can predict the behavior of the production version within a few percent. Luna: So how much can a typical hardware startup save? I've seen numbers like fifty to seventy percent reduction in prototyping costs. Lucas: Depending on the complexity. A medical device startup I spoke with said they cut their iteration cycles from eight to two before regulatory submission. That's a huge time save. time to market is often the real killer for hardware startups—not just the cash burn. Luna: But there's a catch. Digital twin software isn't free. Ansys licenses can run five figures a year. COMSOL, Siemens Simcenter—they're expensive. What's the entry point for a bootstrapped team? Lucas: Great question. There are open-source options. OpenModelica is a solid choice for system-level simulation. For FEA and CFD, you've got CalculiX and OpenFOAM. They have steeper learning curves, but the cost is zero. And some cloud-based platforms offer pay per use pricing. SimScale, for example, lets you run simulations without buying a license. Luna: You mentioned Tesla earlier. They famously use digital twins for over-the-air updates—every car has a twin that mirrors its battery health, motor performance, even the infotainment system. They can push a software update to improve range or fix a bug without a recall. Lucas: That's the operational phase. But Tesla also uses digital twins in design. The Cybertruck's exoskeleton—they simulated crash loads thousands of times before building a single vehicle. That's the same logic AeroVane used: fail fast in software. Luna: So for a hardware startup, the question isn't whether to adopt digital twins—it's when. And the answer seems to be: before your first physical prototype. Lucas: Before the first prototype, or at least in parallel with it. Build a simple model in week one. By week three, you'll have a simulation that can run a hundred variations of a parameter—say, the stiffness of a bracket—and tell you which one avoids resonance. That's a superpower when you're competing against established players. Luna: There's also a trust angle. Investors and early customers want to see that you've de-risked the product. Showing a digital twin with validation data is often more convincing than a non-functional prototype. Lucas: Absolutely. A digital twin lets you demonstrate performance before you have a physical unit. I've seen startups win pilot programs by running a live simulation of their device in the customer's environment—showing how it would behave in their specific factory or clinic. Luna: Which brings up the data side. Once you have a deployed product, the digital twin becomes a monitoring tool. You can predict when a component will fail, schedule maintenance, even optimize energy use. That's where the long-term value lives. Lucas: Siemens does this with gas turbines. They have digital twins of turbines in power plants around the world. The twin ingests real-time sensor data and alerts operators to anomalies before a failure occurs. That saves millions in downtime. Luna: But for a startup, building a full operational digital twin might be overkill early on. Start with the design twin, validate it, then evolve. The tools are modular. Lucas: Right. And that modularity is why I think digital twins are finally hitting the mainstream for hardware startups. The barrier used to be that you needed a PhD in computational physics and a six-figure software budget. Now you can get started with a laptop and some open-source tools. Luna: And yet, a lot of hardware founders I talk to still skip simulation. They want to 'just build it'—which is understandable. There's a tactile satisfaction in making something real. Lucas: But the 'just build it' approach is exactly why so many hardware startups fail. They run out of money during iteration. Digital twins don't replace physical testing entirely—you still need to validate the simulation with real-world data—but they drastically reduce the number of physical iterations needed. Luna: And that reduction is the difference between a startup that makes it to production and one that dies in prototyping. Speaking of which—we've been talking about how expensive simulation software can be, and that's actually one of the reasons this podcast exists. We deliberately don't run ads on these episodes. If you want to support that choice, the link is buy me a coffee dot com slash fexingo. Lucas: Yeah, we want these conversations to be accessible to anyone building hardware—no paywalls, no sponsored segments. Listener support is what keeps it that way. Appreciate anyone who chips in. Luna: Alright, back to digital twins. One more use case I find fascinating: using a digital twin to model your supply chain. It's not just the product itself—you can simulate how changes in component lead times or shipping routes affect your production schedule. Lucas: That's a whole other layer. Some startups are building 'digital twins of the factory'—simulating the entire production line before they ever install equipment. They can identify bottlenecks, optimize layout, and even train operators in a virtual environment. It's like SimCity for manufacturing. Luna: And that reduces ramp-up time. When you finally turn on the real line, it's already tuned. That's huge for hardware startups that often struggle with the transition from prototyping to volume manufacturing. Lucas: One caution, though: digital twins are only as good as the data feeding them. Garbage in, garbage out. Startups need to invest in sensors and data collection from the start. If your simulation is based on inaccurate assumptions, it'll give you false confidence. Luna: So the skill isn't just running the software—it's knowing what to model and how to validate. That's where having a good systems engineer on the team matters. Lucas: Exactly. And that brings us full circle. The digital twin is a tool, not a magic wand. But used correctly, it can be the difference between burning through your seed round on prototypes and reaching production with cash in the bank. Luna: I think the key takeaway for our listeners is: if you're starting a hardware company, build a simple digital twin in week one. Even if it's just a block diagram with transfer functions. It will pay for itself the first time it catches a mistake. Lucas: And as the tools get cheaper and easier to use, I suspect we'll see digital twins become as standard as CAD in hardware development. It's just smarter engineering. Luna: Next time, we'll look at how hardware startups are using generative design to optimize for weight and strength—another simulation-driven approach that's reshaping product development. Lucas: Looking forward to that. For now, if you're iterating on a physical product, consider giving simulation a shot. Your bank account will thank you.