The most interesting question in this student's demand forecasting project isn't about algorithms, it's about architecture. When you're scaling predictions across 40 locations with 1.1 million records, the choice between one global model and 40 local models defines everything that follows. And the smart answer, as we see it, is neither extreme.
A global model trained on all locations simultaneously risks exactly what the student fears: averaging out regional patterns until every forecast becomes a bland compromise. A local model for each location, meanwhile, ignores the shared structure that makes retail demand predictable in the first place. Seasonal spikes, promotional cycles, weather dependencies, these patterns repeat across locations, and a model that can't learn from one to inform another leaves signal on the table. This is the practical reality of moving beyond parameter count to measure true model complexity, as we've explored in Move Beyond Parameter Count to Measure True Model Complexity. The effective complexity of a demand signal isn't captured by how many locations you have; it's captured by how much structure those locations share.
The hybrid path is the one worth taking. Group similar locations by demand profile, urban versus rural, high-volume versus low-volume, seasonal versus steady, and train a shared model per cluster. Each cluster model learns the common dynamics of its group while preserving location-specific differences through features like store ID embeddings, local holiday calendars, and historical promotion frequency. This approach avoids the averaging trap of a single global model while escaping the data-sparsity problem that cripples 40 separate models when a new product launches in only three locations. The student's instinct to explore grouping is exactly right, and we'd add that time-series validation across locations demands careful handling: never let future information from one location leak into another's training window, and always evaluate on location-specific holdouts, not shuffled random splits.
The synthetic data question deserves equal attention. When the dataset is generated rather than observed, the model can learn the generation process itself rather than the underlying demand reality. This is a version of the problem we highlighted in When AI agents cheat to win, machine learning experiments lose their integrity. If the synthetic data follows a fixed set of rules, a sufficiently complex model will memorize those rules and appear to generalize perfectly, until it meets real-world noise, missing data, or a promotion pattern the generator never imagined. The precaution is simple but non-negotiable: hold out entire locations and entire time periods during evaluation, and test whether the model's performance degrades gracefully when you introduce small perturbations to the input features. A model that shatters under minor noise is memorizing artifacts, not learning patterns.
The concrete takeaway is this: start with a clustered global model using shared features and location-specific embeddings, validate with time-series cross-validation that respects location boundaries, and stress-test your synthetic data by breaking its assumptions. If the model survives that, it's ready for real inventory decisions. If not, you've learned something valuable before a single dollar of stock is at risk.