Demand Forecasting Architecture
End-to-end ML pipeline for inventory optimization. Model choice depends on data volume and pattern complexity.
Click any node for details.
Decision Log
Model Decisions
- Model selection, Start with Prophet/XGBoost; add LSTM only if seasonal patterns are highly complex.
- Granularity, SKU-level for fast movers; category rollup for slow movers or new products.
- Horizon, Lead time + safety buffer; typically 2-12 weeks depending on supply chain.
- Ensemble approach, Average multiple models for production; single model for interpretability needs.
MLOps & Integration
- Retraining cadence, Weekly for stable products; triggered for high-drift SKUs.
- Feature store, Feast or Tecton for shared feature engineering across models.
- ERP integration, API or file-based push of recommendations to ordering systems.
- Drift monitoring, Track MAPE degradation and feature distribution shifts.
Overview
Inventory is your biggest working capital sink. We build forecasting models that predict demand accurately; so you order what you need, when you need it.
Model Comparison
| Model | Best For | Strengths | Limitations |
|---|---|---|---|
| SARIMAX | Stable, seasonal products | Interpretable, handles trends | Needs stationarity |
| Prophet | Products with holidays/events | Easy tuning, robust to missing data | Less accurate for complex patterns |
| XGBoost | High-volume with rich features | Handles many features, fast | Needs careful feature engineering |
| LSTM | Complex temporal patterns | Captures long dependencies | Data hungry, harder to tune |
| Ensemble | Production use | Combines strengths | More complexity |
Inventory impact
Reduced overstock
25-40% reduction in excess inventory carrying costs.
Fewer stockouts
Better fill rates through accurate demand prediction.
Cash flow improvement
Free up working capital tied up in inventory.
Automated replenishment
AI-driven purchase order suggestions.
Forecasting services
- •Demand forecasting with SARIMAX, Prophet, XGBoost, or deep learning (LSTM/Transformer)
- •Feature engineering with holiday calendars, promotions, and external signals
- •Safety stock optimization based on service level targets
- •MAPE/MAE/WAPE tracking with automated model retraining
- •Feature store for ML feature management
- •MLOps monitoring for model drift and performance degradation
- •Integration with ERP/WMS for automated recommendations
Forecasting scenarios
Forecasting FAQs
What data do you need?
At minimum, 2+ years of historical sales data. Ideally, we also have inventory levels, promotions, pricing, and external factors like weather or events.
How accurate are the forecasts?
Accuracy depends on data quality and product dynamics. Typical MAPE (Mean Absolute Percentage Error) ranges from 15-30% for SKU-level forecasts.
Can you forecast new products?
Yes; we use similarity-based methods and category-level models to cold-start forecasts for new SKUs.