There’s usually a fair few features that can be incorporated into demand prediction: trend, seasonality (daily, weekly, monthly..), special events (e.g. Easter eggs selling faster near Easter), weather, restocking time etc.
I imagine you could get decent improvements in prediction accuracy using a more sophisticated ML model.
That said even the simple sale velocity model you describe has learnable parameters (e.g. historical data window length). This would probably be best done using an ML rather than ad hoc approach. An ML approach might be as simple as a logistic model indicating whether an item will be in stock or out of stock based on time of day + stock levels at the start of the day.
I imagine you could get decent improvements in prediction accuracy using a more sophisticated ML model.
That said even the simple sale velocity model you describe has learnable parameters (e.g. historical data window length). This would probably be best done using an ML rather than ad hoc approach. An ML approach might be as simple as a logistic model indicating whether an item will be in stock or out of stock based on time of day + stock levels at the start of the day.