# Demand bottom-up forecasting and substitution effect

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.

Update

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;
• 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.

• thank you, I have Rob's book, will have a look. In term so the Guisti & Batista paper it does not seem to be very relevant, because what I am looking for is understanding of the "distance" through substitution level (pure sales history) rather than using attributes of the product. If I am to use Euclidian distance to identify units of demand I will end up with Product Type / Product Category / Colour as ID of a cluster which is a good start, but not independent from the human input. Or did I get it wrong? – Bulat - Reinstate Monica Aug 3 '15 at 23:27