Identifying the Friction
This heritage French fashion house was destroying 40% of seasonal inventory to protect brand equity. Their 47 boutiques across Europe had wildly imbalanced stock—Paris and Milan ran out of bestsellers while regional stores sat on slow-moving pieces that eventually had to be incinerated.
Intelligent Intervention
We deployed our Predictive Allocation AI that analyzes local demand signals, customer fit data, and regional fashion preferences. The system automates inter-store transfers, moving inventory from low-velocity to high-velocity locations before the markdown window opens.
"For the first time in our 85-year history, we ended a season with less than 8% unsold inventory. The AI understood our boutiques better than we did."
Dr. Elena Vance
Chief Data ScientistFormer ML Researcher at Stanford with 15+ years in predictive analytics. Elena leads our core algorithm team, focusing on demand sensing and reinforcement learning for supply chains.
ConnectReady to replicate these results?
Our models are ready to be trained on your data. Start your transformation today.