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Hi,
I wonder someone can help.
I am working on Inventory Health to acertain overstocks.
I worked out my stock weeks cover by simply looking at my balance on hand devided by average week sales. I than created a new column to ascertain the following 'overstock brackets':
- 12-16 weeks
- 16-26 weeks
- 26-56 weeks
- 56+ weeks
- No Demand
I now need to ascertain what porpotion of balance on hand quantity is associated to each of the above bracket.
I am mindful that one bracket can overlap other bracket; meaning I can have portion of balance on hand in bracket 16-26 weeks and 26-56 weeks.
Can anyone help how to work this out in PowerBI?
Thank you.
Tomas
@tomasv
Please show your logic with reasonable sample data to get the expected "121","164", etc.
Read this post to describe your sample:
https://community.powerbi.com/t5/Community-Blog/How-to-Get-Your-Question-Answered-Quickly/ba-p/38490
Paul Zheng _ Community Support Team
thanks for your feedback.
Please see below sample data for all overstock scenarios.
It's worth noting that 'no demand' bracket is generated if ProductID have no sales.
ProductID | Price/Unit (£) | Balance on Hand | Avg Wk Sale (Units) | Avg Wk Sale (£) | Wks Cover | Overstock Bracket |
12 | 6.71 | 2257 | 178 | 1194.38 | 12.7 | 12-16 |
33 | 5.64 | 392 | 19 | 107.16 | 20.6 | 16-26 |
67 | 20.8 | 989 | 1 | 20.8 | 989 | 56+ |
116 | 27.22 | 1557 | 1412 | 38434.64 | 1.1 | |
133 | 92.43 | 41 | 0 | 0 | 1000000 | No Demand |
189 | 0.39 | 4518 | 171 | 66.69 | 26.4 | 26-56 |
@tomasv
Please include the expected results as well as column
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Please see below results. - it's driven by basic if statement to identify stock on hand that is associated to each overtock criteria.
ProductID | Price/Unit (£) | Balance on Hand | Avg Wk Sale (Units) | Avg Wk Sale (£) | Wks Cover | Overstock Bracket | 12-16 | 16-26 | 26+56 | 56+ | No Demand |
12 | 6.71 | 2257 | 178 | 1194.38 | 12.7 | 12-16 | 121 | 0 | 0 | 0 | 0 |
33 | 5.64 | 392 | 19 | 107.16 | 20.6 | 16-26 | 164 | 0 | 0 | 0 | 0 |
67 | 20.8 | 989 | 1 | 20.8 | 989 | 56+ | 14 | 26 | 4 | 933 | 0 |
116 | 27.22 | 1557 | 1412 | 38434.64 | 1.1 | 0 | 0 | 0 | 0 | 0 | |
133 | 92.43 | 41 | 0 | 0 | 1000000 | No Demand | 0 | 0 | 0 | 0 | 41 |
189 | 0.39 | 4518 | 171 | 66.69 | 26.4 | 26-56 | 2394 | 72 | 0 | 0 | 0 |
@tomasv
I believe that you can use the groupby function to achieve this. If you could provide dummy or sample data to workout the solution it will be very useful
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