Register now to learn Fabric in free live sessions led by the best Microsoft experts. From Apr 16 to May 9, in English and Spanish.
I would like to forecast my costs, based on the average cost in de last 6 months. The outcome of the measure should be presented for al future weeks/months, starting 30 days after today. This is to predict future payables. I will use this forecast in my cashflow prediction, which is a running total of payables, receivables and opportunities.
I created:
Something is going wrong in de running total of my cashflow. I think the date filtering in my expected costs is the reason, but i'm not sure.
The measure Cashflow is calculating the correct amounts in my opinion.
@Oomsen , As you already using a time filter, running total might not work well
Can you share sample data and sample output in table format? Or a sample pbix after removing sensitive data.
or try like
CALCULATE(SUMx(values(Date[Date]),[payables average weekly ] ) ,filter(allselected('Date'),'Date'[date] <=max('Date'[date])))
@amitchandak below same sample data.
receivables are from my ERP
payables are from my ERP
costs are historical from GL payables (ERP)
expected are average costs last 6 months
cashflow is receivables - payables - expected
running total is sum of cashflow
Month | Week | receivables | Payables | Costs | Expected | Cashflow | Running total |
jan | 1 | 50 | |||||
2 | 100 | ||||||
3 | 50 | ||||||
4 | 25 | ||||||
5 | 75 | ||||||
feb | 6 | 50 | |||||
7 | 100 | ||||||
8 | 50 | ||||||
9 | 100 | ||||||
mrt | 10 | 50 | |||||
11 | 25 | ||||||
12 | 75 | ||||||
13 | 50 | ||||||
apr | 14 | 100 | |||||
15 | 50 | ||||||
16 | 100 | ||||||
17 | 50 | ||||||
may | 18 | 25 | |||||
19 | 75 | ||||||
20 | 50 | ||||||
21 | 100 | ||||||
22 | 50 | ||||||
jun | 23 | 100 | |||||
24 | 50 | ||||||
25 | 25 | ||||||
26 | 75 | ||||||
jul | 27 | 50 | |||||
28 | 100 | ||||||
29 | 50 | ||||||
30 | 100 | ||||||
aug | 31 | 50 | |||||
32 | 25 | ||||||
33 | 75 | ||||||
34 | 50 | ||||||
35 | 100 | ||||||
sep | 36 | 60 | 10 | 50 | 50 | ||
37 | 100 | 40 | 60 | 110,00 | |||
38 | 40 | 30 | 10 | 120,00 | |||
39 | 25 | 20 | 5 | 125,00 | |||
40 | 50 | 15 | 35 | 160,00 | |||
okt | 41 | 63,46 | -63,46 | 96,54 | |||
42 | 63,46 | -63,46 | 33,08 | ||||
43 | 63,46 | -63,46 | -30,38 | ||||
44 | 63,46 | -63,46 | -93,85 | ||||
nov | 45 | 63,46 | -63,46 | -157,31 | |||
46 | 63,46 | -63,46 | -220,77 | ||||
47 | 63,46 | -63,46 | -284,23 | ||||
48 | 63,46 | -63,46 | -347,69 | ||||
dec | 49 | 63,46 | -63,46 | -411,15 | |||
50 | 63,46 | -63,46 | -474,62 | ||||
51 | 63,46 | -63,46 | -538,08 | ||||
52 | 63,46 | -63,46 | -601,54 | ||||
Any suggestions?
Covering the world! 9:00-10:30 AM Sydney, 4:00-5:30 PM CET (Paris/Berlin), 7:00-8:30 PM Mexico City
Check out the April 2024 Power BI update to learn about new features.
User | Count |
---|---|
109 | |
98 | |
77 | |
66 | |
54 |
User | Count |
---|---|
144 | |
104 | |
101 | |
86 | |
64 |