AI-Driven Demand Prediction Platform
Global E-CommerceMachine Learning for Remanufactured Products Optimization
Advanced Machine Learning platform with ensemble learning and PDP-driven causal inference to predict customer demand for remanufactured products, enabling optimization of inventory and boosting circular economy adoption.
Key Results:
- >90% prediction accuracy for remanufactured product demand
- 40% reduction in inventory waste
- 25% increase in remanufactured product sales
Technologies Used:
2022 EURO Award Winner for Best EJOR Paper
Leveraged EURO award-winning methodology for circular economy optimization.
Challenge
Predicting customer demand for remanufactured products to optimize inventory and boost circular economy adoption. Complex non-linear market dynamics required advanced analytics to decode online market factors and their impact on sales.
Solution
Advanced Machine Learning models with ensemble learning and PDP-driven causal inference to decode non-linear dynamics. Real-time analysis of Amazon transaction data with >90% prediction accuracy for remanufactured product demand optimization.
Key Features
- Ensemble Learning models for demand prediction
- PDP-driven causal inference for market analysis
- Real-time Amazon transaction data processing
- Advanced analytics for circular economy optimization