Don’t just say “damaged”—AI vision can now document, score, and categorize return conditions for claims, QA, and prevention
Glass returns often arrive with one word on the RMA: “Damaged.” But what does that mean?
Impact cracks?
Laminate delamination?
Corner chipping from racking?
Surface scratches from transit friction?
Without standardized classification, your returns team can’t improve packing, QA can’t trace the root cause, and freight claims are weaker. That’s why more distributors are using AI-powered computer vision to classify returned glass by damage type—and create an objective record.
The Old Way vs. the AI Way
Old:
Worker eyeballs the return, snaps a photo, logs “broken”
Team guesses the cause
Claim gets filed with vague supporting docs
AI Way:
High-res camera scans the panel
AI identifies crack type, pattern, edge wear, surface scratches
Damage is scored and logged with time, SKU, and location
Output: “Crack Type: Internal Flex, Likely Transit Vibration > 3mm Offset”
What Vision AI Systems See
Edge Chip Detection
Recognizes micro-cracking and flaking common during crate jostling
Surface Scratch Mapping
Scores scratches by depth, length, and location—linked to potential racking issues
Fracture Pattern Classification
Identifies thermal vs mechanical breaks based on crack shape and spread
Image-Backed Documentation
Damage is logged with image ID, timestamp, and SKU—perfect for freight claims or QA feedback
Real-World Result: Architectural Glass Distributor (Vancouver)
Implemented AI scanning station for high-value returned panels
Return classification accuracy rose from 54% (manual) to 97%
Carrier claim approval rate improved by 62%
Packing SOPs updated to address most frequent causes of damage
How to Set It Up
Install vision stations near RMA processing zone
Train AI with tagged images from past returns
Connect system to WMS for instant SKU matching
Build claim and QA workflows off the AI classification output
A return is never ideal—but when it happens, you deserve clarity. AI vision now gives your team the data to learn from every damaged panel—and prevent the next one.
Don’t just process returns. Understand them. With AI, you can.
