Procurement planning based on Excel spreadsheets and intuition — classic logistics. The result: excess stock in warehouses alongside shortages of fast-moving items, emergency orders with premium tariffs. We develop comprehensive AI systems for logistics and supply chains that shift logistics from reactive to predictive mode. The system knows about a shortage 3-4 weeks before it happens and automatically adjusts procurement plans. One of our clients — a retail chain with 2000 SKUs — after implementation reduced operational budget by 18% and improved OTIF from 84% to 94%. Savings amounted to $405k–585k per year on a project budget of 8 million.
How AI forecasts demand in the supply chain?
Demand forecasting is a multi-level challenge: from SKU to supplier and transport leg. Errors at each level multiply, causing the bullwhip effect (bullwhip effect). We use hierarchical time series forecasting with bottom-up and top-down reconciliation. Base models — AutoARIMA, AutoETS, CrostonOptimized — are combined via MinTrace OLS:
from statsforecast import StatsForecast from statsforecast.models import AutoARIMA, AutoETS, CrostonOptimized from hierarchicalforecast.methods import MinTrace from hierarchicalforecast.core import HierarchicalReconciliation models = [AutoARIMA(), AutoETS(), CrostonOptimized()] sf = StatsForecast(models=models, freq='W', n_jobs=-1) forecasts_df = sf.forecast(df=panel_data, h=12) hrec = HierarchicalReconciliation(reconcilers=[MinTrace(method='ols')]) reconciled = hrec.reconcile( Y_hat_df=forecasts_df, Y_df=historical_data, S=summing_matrix, tags=hierarchy_tags ) More about forecast reconciliation
The MinTrace method minimizes error variance across the hierarchy. Choosing the OLS solver gives a closed-form solution without iterations.The top-level forecast (category) is aggregated from SKU forecasts. MinTrace ensures consistency across all levels. Result: MAPE accuracy 10-18% vs. 25-35% without reconciliation — that is 1.5-2 times better.
Why is inventory optimization important?
The newsvendor problem for stochastic demand: determine the optimal order quantity considering overstock and shortage costs. ML extends the classic approach: Conditional Value-at-Risk in the objective protects against worst-case scenarios, multi-echelon inventory optimization synchronizes stock across the network, and Reinforcement Learning adapts policies to seasonality and promotions. Applying these methods reduces inventory days by 30% and lowers holding costs — budget savings reach 20% on test purchases.
Transport flow optimization — VRPTW (NP-hard). We use Google OR-Tools for problems up to 500 stops and Large Neighborhood Search with ML heuristics for large networks. Deep Learning (Attention Model) provides an end-to-end solution but requires fine-tuning to the customer's specifics. Result: delivery cost reduction of 8-15%.
| KPI | Baseline | After AI |
|---|---|---|
| OTIF | 82-88% | 92-96% |
| Inventory days | 45-60 | 28-38 |
| Delivery cost | 100% | 85-92% |
| Perfect Order Rate | 75-82% | 88-94% |
| Forecast accuracy (MAPE) | 25-35% | 10-18% |
Risk management with GNN and Knowledge Graph
Early disruption warning. NLP parsing of news, AIS vessel delay data, Baltic Dry Index — the system monitors external signals and builds a supplier Knowledge Graph. GNN predicts the likelihood of cascading failure when a key node fails. Diversification Score via Herfindahl-Hirschman Index assesses risk concentration and suggests diversification scenarios with ROI.
Digital twin for simulation modeling
The simulation model (AnyLogic, SimPy) runs scenarios: 14-day delay from Shanghai → impact on OTIF. Monte Carlo simulation with real lead time distributions answers "what if" questions. ROI of this approach is confirmed by cases: one project delivered $360k–520k savings in the first year.
How to estimate ROI?
We run a pilot on historical data: digital twin simulation shows forecasted KPIs — OTIF, MAPE, inventory levels. Comparison with baseline demonstrates operational budget reduction of 15-20% with service level growth. The report includes payback period and scaling recommendations.
What is included in the work?
- Architectural documentation: data model description, integration schemes, model specification.
- Model development and training: forecasting, optimization, risk analytics.
- Integration with ERP/TMS/WMS: REST, EDI, BAPI.
- Client team training: workshops on operation.
- 3 months post-launch support: monitoring, fine-tuning, fixes.
10+ years on the market, 50+ projects in logistics and SCM. Our experience guarantees results. We take turnkey projects: from data audit to model deployment. Contact us to discuss your project and get a pilot in 3-4 months.
Efficiency metrics
Our hierarchical forecasting approach reduces MAPE by 40% relative to standard ARIMA. Compare with alternatives:
| Approach | MAPE | Implementation cost |
|---|---|---|
| Classic ARIMA | 25-35% | Low |
| Hierarchical MinTrace | 10-18% | Medium |
| Deep Learning (NeuralForecast) | 8-14% | High |
Development timeline for a comprehensive platform is 6-10 months, including forecasting, inventory optimization, and routing with ERP integration.
AI does not replace the logistician — it gives them superpowers. Request a consultation to discuss your tasks.