Forecasting Engine
Published:
Challenge
A retail and distribution company struggled with inventory management across multiple warehouses and product categories. Traditional forecasting methods based on simple moving averages failed to capture seasonal patterns, promotional impacts, and market trends. This led to frequent stockouts of popular items and excess inventory of slow-moving products, resulting in lost sales and increased carrying costs. The company needed a sophisticated forecasting system that could predict demand accurately across thousands of SKUs while adapting to changing market conditions.
Solution
Designed and implemented a machine learning-powered forecasting engine with automated model selection and continuous learning:
System Architecture:
- Data Pipeline: Automated ingestion of sales, inventory, and external data (weather, events, economic indicators)
- Feature Engineering: Time-series features, lag variables, seasonal decomposition, promotional indicators
- Model Ensemble: Multiple forecasting algorithms (ARIMA, Prophet, XGBoost, LSTM) with automated selection
- Hyperparameter Optimization: Automated tuning using Bayesian optimization
- Forecast Reconciliation: Hierarchical forecasting across product categories and locations
- Monitoring & Retraining: Continuous accuracy tracking with automated model updates
Key Capabilities:
- Multi-horizon forecasting (daily, weekly, monthly)
- Automatic detection of trend changes and anomalies
- Promotional impact modeling
- New product forecasting using similar product patterns
- Confidence intervals and prediction uncertainty
- What-if scenario analysis
- Integration with ERP and inventory management systems
Outcome
- 35% improvement in forecast accuracy (MAPE reduction)
- 25% reduction in stockout incidents
- 20% decrease in excess inventory carrying costs
- $3M annual savings from optimized inventory levels
- 15% increase in sales through better product availability
- Forecasting 50,000+ SKUs across 20 warehouses
- 90% automation of forecasting process (previously manual)
- Forecast generation time reduced from 3 days to 2 hours
Technologies
- Python
- SageMaker
- AWS Lambda
- S3
- Glue (ETL)
- Athena
- QuickSight
- Prophet
- XGBoost
- TensorFlow
- Step Functions
Technical Highlights
The forecasting engine employed advanced ML techniques and automation:
Ensemble Approach: Rather than relying on a single algorithm, the system:
- Trained multiple models for each SKU
- Automatically selected the best-performing model based on historical accuracy
- Combined predictions from multiple models for improved robustness
- Adapted model selection as patterns changed over time
Hierarchical Forecasting: The system generated forecasts at multiple levels:
- Individual SKU level for operational planning
- Product category level for strategic decisions
- Warehouse level for capacity planning
- Company-wide aggregates for financial forecasting
- Reconciliation ensured consistency across all levels
Continuous Learning: The system automatically:
- Monitored forecast accuracy in real-time
- Detected when model performance degraded
- Triggered retraining with updated data
- A/B tested new models before deployment
This comprehensive approach transformed demand forecasting from a manual, error-prone process into an automated, accurate, and adaptive system that continuously improved over time.