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Hello everyone,
I have given the following table. When I sort the table by date in Power BI, this comes up. The order is absolutely correct and is exactly what it is supposed to be.
I created the custom index with Power query
current situation:
| ID | ProductID | Nr | Date | Quantity | Art | Input | Output | Stock | accumulated Stock | Index | Customer | |||||||||||
| 123 | 123 | 1 | 26.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 75 | 1281 | L | |||||||||||
| 123 | 123 | 1 | 26.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 73 | 1282 | M | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 311 | 1284 | N | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 305 | 1286 | O | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 303 | 1287 | P | |||||||||||
| 1084807 | 123 | 1 | 27.04.2022 00:00:00 | 4 | Output | 0 | 4 | -4 | 307 | 1285 | Q | |||||||||||
| 123 | 123 | 2 | 27.04.2022 09:15:33 | 100 | Input | 100 | 0 | 100 | 313 | 1283 | R | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 301 | 1289 | S | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 302 | 1288 | T | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 300 | 1290 | U | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 299 | 1291 | V |
expected situation:
| ID | ProductID | Nr | Date | Quantity | Art | Input | Output | Stock | accumulated Stock | Index | Customer | |||||||||||
| 123 | 123 | 1 | 26.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 75 | 1281 | L | |||||||||||
| 123 | 123 | 1 | 26.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 73 | 1282 | M | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 71 | 1284 | N | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 69 | 1286 | O | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 2 | Output | 0 | 2 | -2 | 67 | 1287 | P | |||||||||||
| 123 | 123 | 1 | 27.04.2022 00:00:00 | 4 | Output | 0 | 4 | -4 | 63 | 1285 | Q | |||||||||||
| 123 | 123 | 2 | 27.04.2022 09:15:33 | 100 | Input | 100 | 0 | 100 | 303 | 1283 | R | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 302 | 1289 | S | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 301 | 1288 | T | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 300 | 1290 | U | |||||||||||
| 123 | 123 | 1 | 28.04.2022 00:00:00 | 1 | Output | 0 | 1 | -1 | 299 | 1291 | V |
Solved! Go to Solution.
Hi @azaterol ,
Has your problem been solved? According to your snapshot, if you want to calculate the accumulated stock based on the date and ID, as you have create a index column seams based on date, you can simply create a measure:
Accumulated Stock =
SUMX (
FILTER (
ALL ( 'Table' ),
'Table'[ID] = MAX ( 'Table'[ID] )
&& 'Table'[Index] <= MAX ( 'Table'[Index] )
),
[Stock]
)
Best Regards,
Community Support Team _ kalyj
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @azaterol ,
Has your problem been solved? According to your snapshot, if you want to calculate the accumulated stock based on the date and ID, as you have create a index column seams based on date, you can simply create a measure:
Accumulated Stock =
SUMX (
FILTER (
ALL ( 'Table' ),
'Table'[ID] = MAX ( 'Table'[ID] )
&& 'Table'[Index] <= MAX ( 'Table'[Index] )
),
[Stock]
)
Best Regards,
Community Support Team _ kalyj
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
how do you get the initial stock? where does the 75 come from?
Proud to be a Super User!
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