Time Intelligence Creating Daily Data Model from Monthly Data

[Anonymous]

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.

[v-juanli-msft]

Hi @Anonymous 

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

[Anonymous]

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

[v-juanli-msft]

Hi @Anonymous 

The second table is what you have in your dataset or what you expect to achieve?

Best Regards
Maggie

[Anonymous]

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.