Is a two-phase model (ensembling/stacking) a valid approach for forecasting product demand?

Question

I am working on a project to forecast food sales for a corporate restaurant. Sales are heavily influenced by the number of guests per day, along with other factors like seasonality, weather conditions, and special events.

The products sold fall into different categories/groups (e.g., sandwiches, salads, drinks). For now, I am focusing on predicting the total number of products sold per group rather than individual item-level forecasts.

Instead of building a single model to predict sales directly, I am considering a two-phase model approach:

1. First, train a guest count prediction model (e.g., using time series analysis or regression models). The model will take into account external factors such as weather conditions and vacation periods to improve accuracy.
2. Use the predicted guest count as an input variable for a product demand prediction model, forecasting the number of products sold per category (e.g., using Random Forest, XGBoost, Prophet or another machine learning model). Additionally, I am exploring stacking or ensembling to combine multiple models and improve prediction accuracy.

My questions:

1. Is this two-phase approach (predicting guests first, then product demand) a valid and commonly used strategy?
2. Are there better techniques to model the relationship between guest count and product demand?
3. Would ensembling or stacking provide significant advantages in this case?
4. Are there specific models or methodologies that work particularly well for forecasting product demand in grouped categories?

Any insights or suggestions would be greatly appreciated!