If retailer has many products the is likely to be a substitution effect within product groups (clusters).
Hence, there is a notion of the "unit of demand" that is supposed to gather products based on the substitution level.
Is there a tool or an algorithm that allows to identify "units of demand" automatically based on the sales history?
That can potentially improve the quality of the bottom-up approach and therefore provide better information for the purchasing decisions.
Similar items shoud follow a similar sales pattern (if environment is static), but they often do absolutely the opposite in the real life. There are plenty of reasons which are usually beyond the available data:
- promotion (price reduction) of one item in a group;
- one item is promoted to the home page;
- generic colour is added to the group;
- some items are out of stock.
All these events will cause increase in sales for a subgroup of items while the rest will suffer due to substitution. So if I take the standard deviation per SKU it will be massive, while for the unit of demand it will be non material. Hope that makes sense.