Alteryx Designer

Find answers, ask questions, and share expertise about Alteryx Designer.

Supply Chain Inventory Forecasting Data

7 - Meteor

I am getting started with the Supply Chain Starter Kit Supply Chain: Predictive Inventory Analysis workflow and having trouble interpreting the data fields within the item transaction data. Would anyone be able to explain the information in each column below? Inventory seems to be the inventory balance and purchases seem to be the quantity purchased during the period, but what is the "added" field represent? I could be missing something simple here but I am trying to replicate for a workflow.


Air Filter0441044
Air Filter13770
Air Filter23610
Air Filter322140
Air Filter42110
Air Filter51560

Hey @trettelap 


I checked with the current owner of the supply chain starter kit @akeller1 


  • Inventory = "Inventory on Hand"
  • Purchased = "Sold" - this is the number of units sold in the given period
  • Added = additional units procured during this period 


Rushi Parikh
Sales Engineer
7 - Meteor

Thank you for the response! So that is what I was originally thinking and makes sense when viewed like that. My confusion is when the dates are added. So Period 0 on 2020-07-08 there were 44 then 7 were sold which results in 37 on 2020-07-01. I would have expected the reverse relationship in the dates, with the later date having the lower inventory number. It is possible there is a reason it is set up this way that I am missing. Do you know why this is set up this way?


Thank you again!


Air Filter04410442020-07-08
Air Filter137702020-07-01
Air Filter236102020-06-24
Air Filter3221402020-06-17
Air Filter421102020-06-10
Wooo! ok... You are correct the number don't relate. It is because each period is independent and identified to a specific store location, even though the table does not have that detail. My guess is that for the purpose of the exercise, it is looking at a consolidated inventory and that the lines are independent of each other, the only identification is the period, usually periods are common time frames for inventory purchasing dates and therefore are a one to many relationship, but in this case it appears that period is a one for one relationship with the "POS transaction date" meaning when a customer bought air filters. This allows for a Omnichannel view and provides a consistent time series which can be explored with the predictive models, otherwise the data would be sporadic and difficult to forecast.