E-commerce
Inventory Optimization Engine
ML-powered demand forecasting to eliminate stockouts and reduce excess inventory
Industry
E-commerce SaaS B
Timeline
4 months from kickoff to full production rollout
Team Size
4 engineers (2 ML/data science, 1 backend, 1 DevOps)
Technologies
6+
Overview
E-commerce SaaS B was struggling with a critical balancing act: too much inventory meant millions in tied-up capital and warehouse costs, while too little led to stockouts and lost sales. Their manual forecasting methods couldn't keep up with their rapid growth across 15 warehouses serving different regions.
The Challenge
As the company scaled from regional to national distribution, their inventory management challenges multiplied: • Managing 50,000+ SKUs across 15 warehouses • Seasonal demand variations that were difficult to predict • Regional preferences requiring localized inventory strategies • $2M+ in excess inventory tying up capital • 12% stockout rate leading to lost sales and customer frustration • Manual forecasting processes taking 40+ hours per week • No real-time visibility into optimal reorder points • Supply chain disruptions requiring rapid replanning
Our Solution
We designed and implemented an end-to-end ML-powered inventory optimization system: **Demand Forecasting Engine:** • Built custom TensorFlow models incorporating multiple data sources • Time-series analysis using LSTM networks for seasonal patterns • Regional demand modeling accounting for local preferences • External factor integration (weather, holidays, promotions) • Ensemble approach combining multiple forecasting methods **Optimization Algorithm:** • Multi-warehouse inventory allocation optimization • Dynamic reorder point calculations based on lead times • Safety stock optimization balancing cost vs. service level • Automated purchase order generation and recommendations **Real-Time Dashboard:** • FastAPI backend serving real-time inventory recommendations • Redis caching for sub-second response times • Interactive visualizations showing forecast accuracy and trends • Alert system for potential stockouts or overstock situations • What-if scenario modeling for strategic planning **Infrastructure:** • Kubernetes deployment for high availability • Automated daily model retraining with latest data • PostgreSQL for inventory and sales data • Integration with existing ERP and warehouse management systems
Technologies Used
Impact & Results
$2M+
Annual Cost Savings
30%
Inventory Reduction
99.9%
Fulfillment Rate
< 0.5%
Stockout Rate
92%
Forecasting Accuracy
40 hours/week
Time Savings
The inventory optimization engine has been a game-changer. We've reduced our inventory carrying costs by 30% while actually improving our fulfillment rate. The system pays for itself many times over.
James Chen
VP of Operations, E-commerce SaaS B
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