400,000 active exotic car parts. Only 25% are used parts with correctly identified OEM numbers
Anyone in the automotive parts business knows that an OEM part number is the closest thing to a universal search key. Know the number, and you have a very good chance of finding the right part — or selling the one sitting in your warehouse. Of course, an OEM number doesn’t always guarantee fitment, but it dramatically improves identification and search accuracy. With new parts, this is usually straightforward. With used Ferrari, Lamborghini, McLaren, and other exotic car parts, it’s a completely different story.
How are used parts identified today?
The traditional workflow looks like this: open the manufacturer’s parts catalog → find the vehicle → locate the right exploded diagram → identify the component → get its OEM number. Sounds simple. But when you’re dealing with hundreds or thousands of parts, it quickly becomes a massive manual process.
That’s why many sellers take shortcuts. Instead of an OEM number, they write something like “Ferrari 458, 2012.” The problem? What looks like the same part can have dozens of variations with different OEM numbers depending on the model year, trim, and configuration. Sometimes sellers simply copy the first number they find stamped on the part — which might be a supplier number, an internal code, or something completely unrelated to the OEM catalog. And sometimes listings are even worse: a photo with a description like “Ferrari part. For sale. DM me.” No part name, no model, no number. Good luck finding that listing if you’re the actual buyer.
The traditional workflow looks like this: open the manufacturer’s parts catalog → find the vehicle → locate the right exploded diagram → identify the component → get its OEM number. Sounds simple. But when you’re dealing with hundreds or thousands of parts, it quickly becomes a massive manual process.
That’s why many sellers take shortcuts. Instead of an OEM number, they write something like “Ferrari 458, 2012.” The problem? What looks like the same part can have dozens of variations with different OEM numbers depending on the model year, trim, and configuration. Sometimes sellers simply copy the first number they find stamped on the part — which might be a supplier number, an internal code, or something completely unrelated to the OEM catalog. And sometimes listings are even worse: a photo with a description like “Ferrari part. For sale. DM me.” No part name, no model, no number. Good luck finding that listing if you’re the actual buyer.
Now look at it from the buyer’s side
Finding a used part means going through essentially the same process: manufacturer catalog → exploded diagram → OEM number → searching eBay, Facebook, specialized shops, and dozens of other platforms. And even when you find a match, there’s still the question: is the OEM number correct, and will the part actually fit? Fitment can depend on VIN, vehicle configuration, production date, and OEM supersessions.
Now for some real numbers from SuperCars.Parts
There are currently around 400,000 exotic car parts in our database. More than 35% (~150,000) have an OEM number listed, but approximately 20,000–30,000 of those numbers are incorrect. Another ~20,000 are new parts, where identification is generally less of an issue.
That leaves roughly 100,000 used parts with correctly identified OEM numbers — just 25% of the entire database.
This isn’t just a data quality problem. It’s a real business problem. Buyers can’t find the parts they need. Sellers lose potential sales. And parts departments waste hours answering the same question over and over again: “Will this fit my car?”
This is exactly the problem I’m tackling with big data and AI
I’m currently finalizing the integration of SuperCars.Parts with manufacturer parts catalogs. This will allow me to connect real-world listings with original OEM numbers, exploded diagrams, and vehicle fitment data.
The next step is teaching AI to identify OEM numbers and determine fitment even when the seller provides almost no information — using photos, part markings, manufacturer catalogs, and data collected from hundreds of thousands of parts.
The goal is simple: sellers shouldn’t have to manually identify every single part, and buyers shouldn’t have to become detectives just to find one.
Finding a used part means going through essentially the same process: manufacturer catalog → exploded diagram → OEM number → searching eBay, Facebook, specialized shops, and dozens of other platforms. And even when you find a match, there’s still the question: is the OEM number correct, and will the part actually fit? Fitment can depend on VIN, vehicle configuration, production date, and OEM supersessions.
Now for some real numbers from SuperCars.Parts
There are currently around 400,000 exotic car parts in our database. More than 35% (~150,000) have an OEM number listed, but approximately 20,000–30,000 of those numbers are incorrect. Another ~20,000 are new parts, where identification is generally less of an issue.
That leaves roughly 100,000 used parts with correctly identified OEM numbers — just 25% of the entire database.
This isn’t just a data quality problem. It’s a real business problem. Buyers can’t find the parts they need. Sellers lose potential sales. And parts departments waste hours answering the same question over and over again: “Will this fit my car?”
This is exactly the problem I’m tackling with big data and AI
I’m currently finalizing the integration of SuperCars.Parts with manufacturer parts catalogs. This will allow me to connect real-world listings with original OEM numbers, exploded diagrams, and vehicle fitment data.
The next step is teaching AI to identify OEM numbers and determine fitment even when the seller provides almost no information — using photos, part markings, manufacturer catalogs, and data collected from hundreds of thousands of parts.
The goal is simple: sellers shouldn’t have to manually identify every single part, and buyers shouldn’t have to become detectives just to find one.
November 12, 2026