The label-printing challenge is not any single defect but the sheer SKU count. With thousands of SKUs and tiny order batches, every extra ten minutes of changeover costs the line a batch. DaoAI cuts per-SKU modeling to 20–50 golden samples, returning changeover to the minute scale.
A hardware-tools plant prints adhesive labels for a large range of tools and fittings, involving barcodes, spec parameters, warning icons and multilingual text. SKUs number in the thousands, mostly small batches and rush orders. Label defects include missing print, wrong print, color-registration drift, unreadable barcodes and blurred text; any escape can cause customer mis-sorting or compliance issues.
The core tension is very high SKU count against very small batches. Each SKU change requires reconfiguring inspection; if changeover takes an hour or two, the line spends huge time switching rather than producing. Labels also demand high precision—both high detection and low false reject—since excessive false rejects scrap good product and eat into already thin per-batch margins.
DaoAI solution: model each SKU from 20–50 golden samples, complete changeover in 30 minutes
DaoAI applies golden-sample management for high-SKU scenarios: each SKU needs only 20–50 golden samples to build a model, the model library is archived by SKU, and changeover simply recalls the matching golden-sample baseline. With AI-AOI general appearance inspection, characters, barcodes, color registration and icons are all verified in one flow.
- Each SKU needs only 20–50 golden samples; the model library is archived by SKU for instant recall
- Changeover completes within 30 minutes, sharply cutting switching loss on small-batch orders
- Label defect detection reaches 99.5%, covering missing print, wrong print, registration drift and barcode anomalies
- False-reject rate is held under 0.3%, preserving good-product yield on small batches
The more SKUs, the more golden-sample management pays off—changeover returns from hours to minutes.
After deployment the line switches quickly even under thousands of SKUs and frequent rush orders, with changeover steady within 30 minutes. The 99.5% detection rate intercepts wrong and missing-print labels before shipment, while a sub-0.3% false-reject rate preserves good-product yield on small batches, raising overall capacity and quality together.