My name's Nicolas and it's my first message on this forum, glad to be part of the community
My knowledge of QlikSense is above average, but I'm struggling to build an application for my work, and was hoping that you guys could help me out.
Here is my situation. I work in Supply Chain management, and we would like to have a vision of WHEN a product that we are using in our fabrication lines is going to be missing. Here are the informations I can extract from SAP :
- Stock level for this item
- Average comsumption (how many items are we using on, let's say, a monthly basis)
- Incoming orders (the quantity that we are supposed to receive from our suppliers, and when we are supposed to receive it)
- Item description/etc...
Let's take an article with the following characteristics :
- reference : A
- stock level on CW 20: 100 pieces
- monthly consumption of 50 pieces (ie, weekly consumption of 50/4 = 13, rounded)
- next ordered quantity arriving CW 22 with 100 pieces
I would like to try to build something that would show me, for every week (even for the weeks where nothing happen), the "level" of the part : for CW 20, the "level" would be level = stock + incoming order - consumption, so (level CW 20) = 100 + 0 - 13 = 87.
(level CW 21) must be (level CW 20) + (orders CW 21) - consumption = 87 + 0 - 13 = 74
(level CW22) = 74 + 100 (<= the order is arriving CW 22) - 13 = 161
etc ... until CW 52 year 2020 basically
I have managed to link the tables and reach a star scheme (using outer joins and left joins), but I cant manage once I have summarized all my data in a table to perform the "level" calculation. To obtain it, I must extract the data (and only a part of it, given that I have a memory overflown) to Excel, enter the calculation by hand, and reload the new data to be able to analyze it. And I just want to have a fully fonctionnal application in Qlik Sense!
Could you guys please help me out ?
If needs be, I'll try to post a screenshot of my data model, if my explanations weren't so clear.
Thanks you so much in advance.
With best regards,