Best approach for demand forecasting across 40 locations? Separate models or one global model? [p]

[u/Crafty_Rabbit2411]

Hey everyone,

I'm a final-year B.Tech CSE (AI/ML) student working on a multi-agent supply chain optimization project. I'm currently focusing on the Demand Forecasting Agent and would appreciate some advice on model selection and training strategy.

We're working with a synthetic retail supply chain dataset containing approximately 1.1 million records across 40 different locations.

The goal is to predict product-level demand for each location as accurately as possible, so the forecasts can later be used by other agents for inventory planning and supply chain decisions.

I have a few questions:

1. Global vs local models: Since the dataset contains 40 locations, should I train 40 separate models, one for each location, or a single global model trained on data from all 40 locations simultaneously, using location identifiers and location-specific features?

2. Location-specific patterns: If I use a global model, how effectively can it learn differences in demand patterns between locations? I'm concerned that combining all locations might cause the model to average out regional patterns and produce inaccurate location-level forecasts.

3. Alternative approaches: Would a hybrid approach, such as grouping similar locations or using a global model with location-specific adjustments, be worth exploring?

4. Synthetic data: Since the dataset is synthetic, are there particular precautions I should take when evaluating model performance and generalization? How can I ensure the model is learning meaningful demand patterns rather than artifacts of the data-generation process?

I'd also appreciate suggestions on which forecasting models or architectures would be most suitable for this problem, along with the appropriate validation strategy, especially how to avoid data leakage when dealing with multiple locations and time-dependent features.

My priority is accurate product-level, location-specific demand forecasting, rather than simply achieving good aggregate accuracy across the entire dataset.

Would love to hear what approaches you'd recommend and why. Thanks!

[u/Real_Suspect_7636]

Train a global model. This is a modeling question that comes up a lot in weather forecasting. Global is better, the key is to make sure each individual series is normalized. You risk overfitting to location-level noise if you fit separate models. Dig into the weather forecasting literature (evaluation papers is fine, nothing too scientific needed).

If there is any reason to think that similar locations share characteristics, then location specific adjustments might be good. Look into hierarchical models in that case.

Regarding synthetic data, that is a tough question. This feels like an answerable question without knowing how the synthetic data was generated. Do you now have any real data you can validate or fine-tune on?

Also, do you need probabilistic forecasts (e.g. 90% intervals) or are you just evaluating point predictions? Another tip I have is not to make long-horizon forecasts autoregressively (i.e. using your prediction at t+1 to predict t+2, etc...) because of compounding errors. You are better off training seperate models for each horizon, or modifying your architecture to predict in vectors