ML Demand Forecasting for Retail and Manufacturing
A retailer loses 3-5% of turnover due to out-of-stock, and another 2-4% from inventory write-offs. We built an ML demand forecasting system that reduces both by 30-50% (up to 50 million RUB savings per year for a chain with 500+ stores). Evaluate our approach on your data — contact us for a demo. All projects backed by an accuracy guarantee and ISO 27001 certified data handling.
How ML Solves the Hierarchical Forecasting Problem
Typically, forecasts are needed at multiple levels: company → category → SKU → SKU×warehouse. Our approach uses global deep learning models (DeepAR, N-BEATS, TFT) trained on all time series simultaneously, producing reconciled forecasts via MinT reconciliation. This saves resources: global models are 3-5x more scalable than traditional ARIMA per SKU. Learn more about reconciliation in the article Hierarchical forecasting.
Why Global Models Are More Efficient for Large Retail
Instead of training a separate model for each SKU-warehouse, global models capture common patterns: seasonality, holidays, promo effects. For a client with 10,000 SKUs across 50 warehouses, this reduces computational costs by 100x (estimate from our projects). We use Google's Temporal Fusion Transformer — state-of-the-art for hierarchical forecasting.
| Method | Approach | Advantage |
|---|---|---|
| Global DL (DeepAR, TFT) | Single model on all SKUs | High accuracy, scalability: 3-5x better than ARIMA |
| LightGBM with lags | Can also be global | Faster, interpretable |
| ARIMA/ETS | One model per SKU | Not scalable beyond 100 SKUs |
What to Do with Intermittent Demand?
For rare sales (intermittent demand), standard methods yield MAPE > 200%. We apply Croston, ADIDA, then boosting. In production, we use a model that selects the method based on sales frequency. For cold-start new SKUs, we incorporate transfer learning from similar product groups.
External Factors and Their Inclusion
Promo activity, weather, and competitor actions are critical. Without them, forecast accuracy drops up to 40% on high-promo categories.
| Factor | Source | Typical Demand Lift |
|---|---|---|
| 20% promo discount | Trade calendar | +80-150% |
| Holiday (New Year) | Calendar | +200% per category |
| Hot weather (>25°C) | Weather API | +50% for beverages |
The promo-lift model is a separate task: predicting incremental demand from a specific mechanism (discount vs. BOGOF). We build causal models with double difference of time series, isolating the promo effect from natural fluctuations.
Production Chain
Demand forecast → MRP II → raw material procurement. Integration via API with SAP S/4HANA IBP, Oracle SCM, or Kinaxis. We have over 5 years of experience implementing such solutions with 20+ projects. Average savings per project exceed 10 million RUB due to reduced write-offs and out-of-stock.
Metrics and Backtesting
We use walk-forward validation: train up to date D, forecast 28 days, then slide forward. Metrics: SMAPE (robust to small values), RMSSE (normalized by naive forecast), Bias (important for inventory). Comparison of ML vs. Naive: ML reduces RMSSE by 35% (from recent projects). For cross-validation, we use temporal slices — providing realistic estimates on held-out data.
What's Included in the Project
Detailed deliverables
- Documentation: model card, feature store, pipeline diagram
- API forecast service (gRPC/REST)
- Model training on your historical data
- Integration with WMS/ERP (SAP, Oracle, Kinaxis, or custom)
- 3 months of support and accuracy monitoring
A baseline LightGBM model for 1,000–10,000 SKUs takes 4–6 weeks. A full hierarchical solution with promo-lift takes 4–6 months. Get a consultation for your scenario — we will estimate exact timelines and KPIs after the first backtest.
How to Implement ML Forecasting: Step-by-Step Plan
- Data analysis and SKU-category segmentation.
- Build baseline (naive forecast + ARIMA).
- Develop global DL model or LightGBM with lags.
- Backtest and adjust model.
- Promo-lift model (if needed).
- Integration and API deployment.
- Monitor accuracy and retrain.
We have gone through this process with dozens of clients — minimum ROI from implementation is 200% due to reduced write-offs and increased sales. Discuss your scenario with our engineers.