The luxury resale market faces mounting challenges with counterfeit detection, particularly for popular collections like the Gucci Marmont line. Kakaobuy's procurement teams have implemented a groundbreaking solution: a 56-point QC standardization systemKakaobuy's latest case study.
The Digital QC Framework Architecture
Kakaobuy's spreadsheet-based system integrates three core technological components:
- Standardized Measurement Templates:
- Image Comparison Engine:
- Automated Documentation:
- Image Comparison Engine:
Predictive Authentication via Machine Learning
Historical QC data from over 2,300 authenticated Marmont bags trains Kakaobuy's risk assessment algorithms to:
- Flag suspicious variations in gusset construction patterns
- Identify non-standard GG buckle engraving depths
- Detect deviation in chevron stitch angles exceeding 3° tolerance
Results showed 92% accuracy in pre-screening potential counterfeits before physical inspection, reducing unnecessary handling of high-risk items.
Step-by-Step Implementation Path
The QC digital transformation followed this phased deployment:
| Phase | Duration | Key Achievement |
|---|---|---|
| Baseline Establishment | Weeks 1-2 | Created master image library of genuine Marmont references |
| Template Development | Weeks 3-5 | Built spreadsheet logic for auto-populating technical observations |
| Team Certification | Weeks 6-8 | Trained all inspectors on digital assessment protocols |
"Where we previously needed third-party authentication for borderline cases, the spreadsheet's decision support now provides 80% of answers immediately. The remaining 20% get escalated with complete digital evidence packages." - Kakaobuy Senior Authentication Manager
The system's current version reduces average inspection time from 25 to 15 minutes while increasing first-pass accuracy to 96.7% according to internal QA audits conducted in Q2 2024.