How Scanflow Delivers AI-Powered Hornpad Identification for Daimler at End-of-Line QC

Daimler’s End-of-Line (EOL) hornpad identification process was highly manual and dependent on operator judgment. QC operators had to refer to paper-based job sheets and visually verify whether the correct hornpad was installed for the corresponding truck model. This created risks of human error, inconsistent inspection, slower QC operations, and limited traceability, since inspection results were not digitally recorded. Incorrect hornpad installations could pass QC undetected, potentially resulting in rework, additional costs, and customer-quality issues.

Daimler engaged Scanflow to bring an AI-enabled Hornpad Identification Solution to end-of-line QC, digitizing and automating the process through a Scan → Capture → Verify → Result workflow verified directly against MES.

1. Manual, Judgment-Based Verification QC operators visually verified hornpad correctness against paper job sheets, leaving the outcome dependent on individual judgment rather than a consistent, verifiable check.
2. Paper Job Sheets Slowed QC Referring to paper-based job sheets for the expected truck model and hornpad specification added time to every end-of-line inspection.
3. Inconsistent Inspection Outcomes Because verification relied on visual judgment rather than a standardized check, inspection outcomes could vary from operator to operator.
4. No Digital Record or Traceability Inspection results were not digitally recorded, leaving no centralized, traceable record of what was verified at end-of-line.
5. Incorrect Installations Could Pass QC Undetected Without a consistent, verifiable check, incorrect hornpad installations could pass QC, risking rework, additional cost, and customer-quality issues downstream.

Scanflow proposed an AI-enabled Hornpad Identification Solution that digitizes and automates the EOL QC process through a simple Scan → Capture → Verify → Result workflow. The QC operator uses a Zebra TC53 rugged handheld to scan the VSN barcode from the job sheet. The application retrieves the expected truck model and hornpad specification from Daimler’s MES through a secure API. The operator then captures an image of the installed hornpad, and the Scanflow AI model identifies the hornpad from the image and compares it with the expected MES information. The application provides an immediate OK / NOT OK result and sends the verification outcome back to MES, reducing manual inspection effort while improving accuracy, consistency, and traceability.

• VSN Barcode ScanThe QC operator scans the VSN barcode from the job sheet using the Zebra TC53 handheld, identifying the vehicle at the start of the workflow.
• MES Lookup via Secure APIThe application retrieves the expected truck model and hornpad specification directly from Daimler’s MES through a secure API call.
• Hornpad Image CaptureThe operator captures an image of the installed hornpad using the handheld’s camera.
• On-Device AI IdentificationThe Scanflow AI model identifies the hornpad from the captured image and compares it against the expected MES information.
• Instant OK / NOT OK ResultThe application returns an immediate OK / NOT OK result to the operator at the point of inspection.
• MES Result SyncThe verification outcome, along with associated vehicle and identification information, is sent back to MES, keeping the production record current.

By replacing paper-based, judgment-driven hornpad verification with an AI-powered, MES-verified scan-and-capture workflow, Daimler gains a faster, more consistent, and fully traceable end-of-line QC check.

Key Outcomes

• Fewer QC errors – AI-based comparison against MES specification replaces reliance on operator judgment.
• Faster inspection – the Scan → Capture → Verify → Result workflow completes with on-device processing, without waiting on a backend inference server.
• Consistent outcomes – every hornpad is evaluated against the same MES-sourced expected specification.
• Full traceability – every OK/NOT OK result, along with vehicle and identification information, is recorded back to MES.

Deployment Footprint

• Zebra TC53 rugged handheld as the sole edge device — no separate backend inference server required.
• On-device AI inference for barcode decoding, image preprocessing, and hornpad identification.
• Daimler’s MES DICV Database as the single source of truth for production records and audit trail.

Daimler’s end-of-line hornpad check no longer relies on paper job sheets and operator judgment. Scanflow’s Scan → Capture → Verify → Result workflow validates the VSN barcode against the truck model, compares the installed hornpad against the MES-sourced specification, and returns an instant OK/NOT OK result — with every outcome synced back to MES. The result is a faster, more consistent QC check that closes the gap where incorrect installations could previously go undetected.