When Tire Identification Becomes the Bottleneck

In large independent tire and wheel retail operations, growth doesn’t usually fail because of big, visible problems. It slows down because of small, repeated ones. Tire identification is one of them. 

Reading DOT numbers. Confirming tire size. Checking production dates. Recording the information correctly. It’s a process your teams perform thousands of times a day, quietly, manually, and often differently from one location to the next. 

For a long time, that worked. Until scale made the cracks visible. 

The moment manual processes start to hold you back 

If you operate dozens or hundreds of locations, you’ve likely seen the pattern: 

• One store completes inspections faster than another. 
• One technician records tire data meticulously; another rushes. 
• A recall or audit requires data you technically have, but not in a format you fully trust. 

Individually, none of this feels urgent. Collectively, it creates friction you can’t quite eliminate with training or process updates alone. 

That’s usually the moment leaders start asking a different question:
Is this still the right way to do this? 

From reading tires to recognizing them 

Tire sidewalls were never designed to be read at speed. They’re embossed, low-contrast, often dirty, and exposed to years of wear. 

Anyline approaches tire identification from a different angle: instead of asking people to read tires more carefully, it uses AI-powered computer vision to digitize tire information automatically. With a standard mobile device, tire data such as: DOT / TIN and production date Tire size, Tire make and model can be captured in seconds, directly from the tire sidewall, without manual transcription. 

The workflow is simple, but the impact is massive. 

What this looks like in a real retail environment 

Picture a routine inspection during vehicle intake: a technician opens a mobile inspection app, points the camera at the tire, and the system captures the DOT number and tire details automatically. The data is stored directly in the inspection record—consistent, legible, and immediately available. 

No manual reading. No typing. No interpretation differences between locations. When this happens hundreds of thousands of times a year, the operational effect compounds. 

Proof that this works at scale 

The impact Anyline created isn’t theoretical. In 2021, Anyline supported a mobile tire inspection solution used by Discount Tire, the world’s largest independent tire and wheel retailer, in collaboration with Zebra Technologies.

Technicians used mobile devices to scan tire DOT numbers and capture tread depth during routine inspections. 

That project showed something important for large retailers:
AI-based tire identification can operate reliably across large store networks, high inspection volumes, and real-world conditions. 

The goal was never to replace technicians, but to remove friction from their work. 

Built for the tires you actually inspect 

If you’re wondering whether this works only in ideal conditions, it doesn’t. Anyline’s AI models are trained on millions of real-world tire images. Tires that are worn, dirty, partially damaged, wet, or scanned at awkward angles. Low-contrast and embossed DOT markings are part of the reality, not an edge case. 

That’s why the technology performs consistently in service bays, parking lots, warehouses, and roadside environments. 

Why tire identification is becoming a strategic topic 

As retail operations scale, tire identification quietly shifts from a procedural task to a strategic one. It affects: 

• Inspection throughput 
• Data consistency across locations 
• Visibility into tire age and compliance 
• Readiness for recalls or regulatory change 
• The success of broader digital inspection initiatives 

Many large retailers now treat AI-powered tire identification as baseline infrastructure—not something experimental, but something necessary to operate efficiently at scale. 

A technology that fits into your world 

Anyline is designed to integrate into existing retail environments. 

You can embed tire sidewall scanning into your own applications, deploy it across iOS and Android devices, and choose between on-device or cloud-based processing. Scanning works online and offline and meets enterprise security and data-protection requirements. 

Most retailers start with an evaluation or pilot, measuring speed, data quality, and operational fit before scaling. 

When it’s time to talk 

If any part of this feels familiar, you’re not alone. Many large independent tire and wheel retailers reach the same conclusion: manual tire identification no longer supports where the business is going. 

AI-powered tire sidewall scanning removes a small but persistent bottleneck that quietly affects speed, consistency, and visibility across the operation. 

If you want to explore whether this capability makes sense for your retail environment, book a meeting with our experts to learn more.