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What is the best way to convert monthly projections to compare against daily results? The source is beginning of month, average daily rate, total days running, and total monthly production. I already have a calendar table by date with BOM, EOM, days, etc.
Hi @holeinone10
Could you show some example data?
"month, average daily rate, total days running, and total monthly production"
What are these fields ? columns or measures?
In the same table or not?
Best Regards
Maggie
The data table represents production for various plants by month and by product line which I want to reitterate as a daily average to reflect actual daily rate vs. forecast for the final output in the second table using measures.
LOCATION | PRODUCT LINE | MONTH | RUN DAYS | DAILY PRODUCTON RATE | PRODUCTION |
woodbury | chocolate | 05/01/2019 | 31 | 22,581 | 700,000 |
woodbury | chocolate | 06/01/2019 | 30 | 30,000 | 900,000 |
woodbury | chocolate | 07/01/2019 | 31 | 19,355 | 600,000 |
woodbury | chocolate | 08/01/2019 | 31 | 22,581 | 700,000 |
woodbury | chocolate | 09/01/2019 | 30 | 30,000 | 900,000 |
est by month | actual by date | avg sold by month | |||||
date | production capacity | production forecated | production actual | %cap | contracted | shipped actual | ending inventory |
06/09/2019 | 32,000 | 30,000 | 94% | 26,667 | 46,437 | ||
06/08/2019 | 32,000 | 30,000 | 94% | 26,667 | 43,104 | ||
06/07/2019 | 32,000 | 30,000 | 94% | 26,667 | 39,771 | ||
06/06/2019 | 32,000 | 30,000 | 94% | 26,667 | 36,437 | ||
06/05/2019 | 32,000 | 30,000 | 94% | 26,667 | 33,104 | ||
06/04/2019 | 32,000 | 30,000 | 94% | 26,667 | 29,771 | ||
06/03/2019 | 32,000 | 30,000 | 94% | 26,667 | 26,437 | ||
06/02/2019 | 32,000 | 30,000 | 94% | 26,667 | 23,104 | ||
06/01/2019 | 32,000 | 30,000 | 94% | 26,667 | 19,771 | ||
05/31/2019 | 32,000 | 22,581 | 71% | 23,333 | 16,437 | ||
05/30/2019 | 32,000 | 22,581 | 25,000 | 78% | 23,333 | 24,000 | 17,190 |
05/29/2019 | 32,000 | 22,581 | 19,000 | 59% | 23,333 | 24,000 | 16,190 |
05/28/2019 | 32,000 | 22,581 | 19,500 | 61% | 23,333 | 24,000 | 21,190 |
05/27/2019 | 32,000 | 22,581 | 19,700 | 62% | 23,333 | 24,000 | 25,690 |
05/26/2019 | 32,000 | 22,581 | 19,990 | 62% | 23,333 | 24,000 | 29,990 |
05/25/2019 | 32,000 | 22,581 | 25,000 | 78% | 23,333 | 23,000 | 34,000 |
Hi @holeinone10
The second table is what you have in your dataset or what you expect to achieve?
Best Regards
Maggie
Hopefully, the result.
The data table represents production for various plants by month and by product line which I want to reitterate as a daily average to reflect actual daily rate vs. forecast for the final output in the second table using measures.
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