multi-agent supply chain

Scaling demand forecasting across 40 locations starts with one smart model choice

Scaling demand forecasting across 40 locations starts with one smart model choice. A single global model with strong location features can learn regional patterns without averaging them out, especially if you include location embeddings and demographic indicators. Grouping similar locations as a hybrid approach is worth exploring too. On synthetic data, hold out entire locations during validation to test real generalization. Time-series cross-validation with temporal splits prevents leakage. Consider gradient-boosted trees or a Transformer-based architecture for capturing both local trends and cross-location patterns.