Case Study
StockAI
An intelligent inventory management system leveraging machine learning for demand forecasting, automated reordering, and warehouse optimization. StockAI helps enterprises minimize carrying costs while preventing stockouts.
99.8% real-time accuracy achieved
Inventory Accuracy
35% reduction in holding expenses
Carrying Costs
inventory-system.ts
// case_study/inventory-system export { project }
The Challenge
What the client was facing:
- Predicting demand across 50,000+ SKUs with seasonal variations
- Real-time inventory tracking across 200+ warehouse locations
- Optimizing warehouse layouts for picking efficiency
- Reducing waste for perishable goods inventory
Our Solution
How we addressed these challenges:
- Developed ensemble ML models combining time-series and external factors
- Implemented IoT-based tracking with RFID and computer vision
- Created AI-driven slotting optimization algorithm
- Built expiration prediction system with dynamic pricing integration
# Challenges
- Predicting demand across 50,000+ SKUs with seasona...
- Real-time inventory tracking across 200+ warehouse...
# Solutions
✓ Developed ensemble ML models combining time-series...
✓ Implemented IoT-based tracking with RFID and compu...
Technical summary
// results.metrics()
The Results
Measurable impact delivered
99.8% real-time accuracy achieved
Inventory Accuracy
35% reduction in holding expenses
Carrying Costs
78% decrease in out-of-stock incidents
Stockout Rate
25% improvement in pick rates
Warehouse Efficiency
results.json
{
"status": "success",
"metrics": 4
}
// stack.list()
Technologies Used
The tools and technologies that powered this project:
Python
FastAPI
Vue.js
PostgreSQL
Apache Kafka
TensorFlow
GCP
BigQuery
package.json
{
"dependencies": {
// 8 technologies
}
}