I need to create a weighted average that multiplies a column of volume manufactured for multiple manufacturing plants by a column containing the cost to manufacture at each plant, and returns one weighted average value for a specific product type for all plants.
I've tried adding this as a calculated column using:
=sumx('Plant','Plant'[Cost]*'Plant'[Tonnage])/sum('Plant'[Tonnage])
But this goes row by row, so it doesn't give me the full over riding average that I need for the company. I can aggregate the data, but really want to see the average lined up against individual plant for benchmarking
Any ideas how I can do this?
You can do this in multiple ways. You can either make a single more complex calculation, or you can make a few calculated columns to make the final calculation more transparent. I will pick the latter approach here, because it is more easy to show what is going on. I'm going to use the following DAX functions: CALCULATE, SUM, and ALLEXCEPT.
First, create three new calculated columns.
The first one should contain the [Volume] times [Cost] for each record:
VolumeTimesCost:=[Volume] * [Cost]
The second one should contain the sum of [VolumeTimesCost] for all plants within a given product type. It could look like this:
TotalProductTypeCost:=CALCULATE(SUM([VolumeTimesCost]),ALLEXCEPT([Product Type]))
Using the ALLEXCEPT([Product Type]) removes the filter from all other columns than the [Product Type] column.
The third calculated column should contain the SUM of [Volume] for all plants within a given product type. It could look like this:
TotalProductTypeVolume:=CALCULATE(SUM([Volume]),ALLEXCEPT([Product Type]))
You can then create your measure based on the two calculated columns [TotalProductTypeCost] and [TotalProductTypeVolume].
I hope that helps you solve the issue correctly. Otherwise feel free to let me know!
Related
I am trying to gather various KPI's for salespersons in multiple stores. The goal is to break down store performance into salesperson level.
I am facing an issue when trying to add a hitrate, as this is normally only by store. Number of quotes given / Visitors.
Even though it may not be 100% accurate I still wish to have the KPI by sales person. I am able to do this on a sales person level, but my subtotal for the store is incorrect, as it makes a summation of visitors by sales persons.
Monthly period is to be considered to as sales persons comes and goes throughout the period. Example of what I wish for a Subtotal for the measure "Vis". Store X 370 for month 1,2 & 3. For Store Y 395.
Vis measure = Visitor (Calculation i have tried but gives the wrong result for the store total for the period.)
I have tried various Calculate, Sum, max functions, but nothing seems to provide the result I need.
I hope that someone might be able to help me get along with this.
Example data tables is link as shown below:
enter image description here
Thanks in advance.
This sounds like a case where the HASONVALUE function would be useful.
The idea being that you would the result of that function in an if to determine if you are calculating at your sales person level or a the store level which should contain multiple salespersons. Thus you would have two different calculations, one for the sales person and store combination, and one for the Store level.
Example would be sometime like the following, in this example I am assuming you have a sales person table:
Measure:= IF( HASONEVALUE( Salesperson[Sales Person] ),
[Vis],
[Measure for subtotal]
)
[Measure for Subtotal] would just being the calculation that you want for your store total.
Of course if you filter to just a single sales person, then the totals for the store will just match that sales person.
I am editing my question which I want exactly.
I have two columns Actual Units, Future Units from Fact A and Fact B respectively but at same granular level.I also have Demand Units from Fact B
My requirement is :
1. Projected Units = Coalesce(Actual Units,Future Units)
2. Stock Units = IF(Projected Units > Demand Units,Demand Units,Projected
Units)
3. Stock Rate = (Stock Units/Demand Units)
I cannot join the two facts in the data source view level and do the
calculation there because they are a very huge tables, so I think the
performance would be very slow. If you say that doing the calculations at
the data source view level level is the only way we have, please let me
know.
Did you get this?
When calculating the grand total MDX is summing up A, summing up B, and then comparing them.
If you want the calculation to occur at the row level (checking whether B>A) then edit the Data Source View and add a new calculated column to the table your measure group is based upon. The calculated column should be:
CASE WHEN B>A THEN A ELSE B END
Then create a Sum measure based upon that new column.
This approach will perform much better compared to any completely MDX approach to calculating this at a very detailed grain. If your fact tables had 500,000 rows or less and you had a degenerate Dimension which was the same grain as the grain you need to calculate at, we could possibly do it in MDX. But since you are concerned with SQL query performance I am assuming the tables are big. Just remember that SQL is done once at processing time. MDX is calculated in every query at query time. So do expensive things in SQL when you can.
Am trying to rank groups by summing a field (not a calculated column) for each group so I get a static answer for each row in my table.
For example, I may have a table with state, agent, and sales. Sales is a field, not a measure. There can be many agents within a state, so there are many rows for each individual state. I am trying to rank the states by total sales within each state.
I have tried many things, but the ones that make the most sense to me are:
rankx(CALCULATETABLE(Table,allexcept(Table,Table[AGENT]),sum([Sales]),,DESC)
and
=rankx(SUMMARIZE(State,Table[State],"Sales",sum(Table[Sales])),[Sales])
The first one is creating a table where it sums sales without grouping by Agent. and then tries to rank based on that. I get #ERROR on this one.
The second one creates a table using SUMMARIZE with only sum of Sales grouped by state, then tries to take that table and rank the states based on Sales. For this one I get a rank of 1 for every row.
I think, but am not sure, that my problem is coming from the sales being a static field and not a calculated measure. I can't figure out where to go from here. Any help?
Assuming your data looks something like this...
...have you tried this:
Ranking Measure = RANKX(ALL('Table'[STATE]),CALCULATE(SUM('Table'[Sales])))
The ALL('Table'[STATE]) says to rank all states. The CALCULATE(SUM('Table'[Sales])) says to rank by the sum of their sales. The CALCULATE wrapper is important; a plain SUM('Table'[Sales]) will be filtered to the current row context, resulting in every state being ranked #1. (Alternatively, you can spin off SUM('Table'[Sales]) into a separate Sales measure - which I'd recommend.)
Note: the ranks will change based on slicers/filters (e.g. a filter by agent will re-rank the states by that agent). If you're looking for a static rank of states by their total sales (i.e. not affected by filters on agent and always looking at the entire table), then try this:
Static Ranking Measure = CALCULATE([Ranking Measure], ALLEXCEPT('Table', 'Table'[State]))
This takes the same ranking measure, but removes all filters except the state filter (which you need to leave, as that's the column you're ranking by).
I did figure out a solution that's pretty simple, but it's messier than I'd like. If it's the only thing that works though, that's okay.
I created a new table with each distinct state along with a sum of sales then just do a basic RANKX on that table.
I'm building a cube in MS BIDS. I need to create a calculated measure that returns the weighted-average of the rank value weighted by the number of searches. I want this value to be calculated at any level, no matter what dimensions have been applied to break-down the data.
I am trying to do something like the following:
I have one measure called [Rank Search Product] which I want to apply at the lowest level possible and then sum all values of it
IIf([Measures].[Searches] IS NOT NULL, [Measures].[Rank] * [Measures].[Searches], NULL)
And then my weighted average measure uses this:
IIf([Measures].[Rank Search Product] IS NOT NULL AND SUM([Measures].[Searches]) <> 0,
SUM([Measures].[Rank Search Product]) / SUM([Measures].[Searches]),
NULL)
I'm totally new to writing MDX queries and so this is all very confusing to me. The calculation should be
([Rank][0]*[Searches][0] + [Rank][1]*[Searches][1] + [Rank][2]*[Searches][2] ...)
/ SUM([searches])
I've also tried to follow what is explained in this link http://sqlblog.com/blogs/mosha/archive/2005/02/13/performance-of-aggregating-data-from-lower-levels-in-mdx.aspx
Currently loading my data into a pivot table in Excel is return #VALUE! for all calculations of my custom measures.
Please halp!
First of all, you would need an intermediate measure, lets say Rank times Searches, in the cube. The most efficient way to implement this would be to calculate it when processing the measure group. You would extend your fact table by a column e. g. in a view or add a named calculation in the data source view. The SQL expression for this column would be something like Searches * Rank. In the cube definition, you would set the aggregation function of this measure to Sum and make it invisible. Then just define your weighted average as
[Measures].[Rank times Searches] / [Measures].[Searches]
or, to avoid irritating results for zero/null values of searches:
IIf([Measures].[Searches] <> 0, [Measures].[Rank times Searches] / [Measures].[Searches], NULL)
Since Analysis Services 2012 SP1, you can abbreviate the latter to
Divide([Measures].[Rank times Searches], [Measures].[Searches], NULL)
Then the MDX engine will apply everything automatically across all dimensions for you.
In the second expression, the <> 0 test includes a <> null test, as in numerical contexts, NULL is evaluated as zero by MDX - in contrast to SQL.
Finally, as I interpret the link you have in your question, you could leave your measure Rank times Searches on SQL/Data Source View level to be anything, maybe just 0 or null, and would then add the following to your calculation script:
({[Measures].[Rank times Searches]}, Leaves()) = [Measures].[Rank] * [Measures].[Searches];
From my point of view, this solution is not as clear as to directly calculate the value as described above. I would also think it could be slower, at least if you use aggregations for some partitions in your cube.
I have a query to pull clickthrough for a funnel, where if a user hit a page it records as "1", else NULL --
SELECT datestamp
,COUNT(visits) as Visits
,count([QE001]) as firstcount
,count([QE002]) as secondcount
,count([QE004]) as thirdcount
,count([QE006]) as finalcount
,user_type
,user_loc
FROM
dbname.dbo.loggingtable
GROUP BY user_type, user_loc
I want to have a column for each ratio, e.g. firstcount/Visits, secondcount/firstcount, etc. as well as a total (finalcount/Visits).
I know this can be done
in an Excel PivotTable by adding a "calculated field"
in SQL by grouping
in PowerPivot by adding a CalculatedColumn, e.g.
=IFERROR(QueryName[finalcount]/QueryName[Visits],0)
BUT I need give the report consumer the option of slicing by just user_type or just user_loc, etc, and excel will tend to ADD the proportions, which won't work b/c
SUM(A/B) != SUM(A)/SUM(B)
Is there a way in DAX/MDX/PowerPivot to add a calculated column/measure, so that it will be calculated as SUM(finalcount)/SUM(Visits), for any user-defined subset of the data (daterange, user type, location, etc.)?
Yes, via calculated measures. calculated columns are for creating values that you want to see on rows/columns/report header...calculated measures are for creating values that you want to see in the values section of a pivot table and can slice/dice by the columns in the model.
The easiest way would be to create 3 calculated "measures" in the calculation area of the powerpivot sheet.
TotalVisits:=SUM(QueryName[visits])
TotalFinalCount:=SUM(QueryName[finalcount])
TotalFinalCount2VisitsRatio:=[TotalFinalCount]/[TotalVisits]
You can then slice the calculated measure [TotalFinalCount2VisitsRatio] by user_type or just user_loc (or whatever) and the value will be calculated correctly. The difference here is that you are explicitly telling the xVelocity engine to SUM-then-DIVIDE. If you create the calculated column, then the engine thinks you want to DIVIDE-then-SUM.
Also, you don't have to break down the measure into 3 separate measures...it's just good practice. If you're interested in learning more, I'd recommend this book...the author is the PowerPivot/DAX guru and the book is very straightforward.