Looking for some vocabulary to help me refine my research so I can tackle this problem.
Here's an overview of the problem statement I'm working on.
At my company we manufacture various products, each product can have a number of different configurations, let's call each unique configuration of a product a PIN (this set up pretty much allows for unlimited customization).
Each product belongs to one of n discrete product classes.
We don't actually know the weights of any particular PIN, however we have years of shipping data and when things get shipped we have a weight associated with the entire shipment.
A shipment can contain several cartons and each carton can contain many different PINs (coming from any one of the n discrete product classes).
I have a nice, clean dataset which contains Shipment ID, Carton number, Product Class, PIN, Number of PINs in Carton, Number of items in Carton, and Total Weight of Carton.
Using this data my job is to estimate the weight of any given PIN.
I don't have a robust enough vocabulary to start my search, and I am hoping someone can help me with either some keywords, or relevant articles that I can look into that might help me make progress against this.
Warning: everything below is literally me thinking out loud
So each PIN is a '/' delimited string (all of them have 11 total '/') which denote the various combinations/configurations a product can take on.
So the easiest version of the problem looks like this - one shipment, with one carton, containing one PIN.
Then its pretty easy to get the weight of one PIN directly.
What I am currently thinking of doing is very similar to that easiest case, identify all shipments with one carton and one PIN.
Aggregate up to the product class level, calculate the mean weight, use that as a starting point.
It's looking like its going to be a situation where I may need to develop 13 separate models for the 13 different product classes