DAX to handle cycles of different months - excel

Question

I have 2 tables.
First table: dimensional table to show available units of cars at start of selling cycle and for how long these units will be available.
Second table: to show how many cars were sold on a given month within their "available cycle".
I'd like to compare the "selling behaviour" within each cycle. Thus, I want to display the total initial units available next to the units sold at each stage within the cycle. The second dimension works fine, but not the first one.
This is what I get:
And this the desired output (note rows 4 and 5 for available_units)
I tried the below DAX code without success:
SumAvailableUnits:=CALCULATE(SUM([available_units]),FILTER(ALL(Table1[month_within_cycle]),[month_within_cycle]>=MAX([months_available])))

Answer 1

First, DAX Formatter is your friend. You may like writing unreadable single line measures, but no one else likes reading them.
I've also taken the liberty of cleaning up your table names and adding fully qualified column references. (Ignoring that your dimension isn't a pure dimension, as it holds numeric values that you aggregate in a measure)
SumAvailableUnits :=
CALCULATE (
SUM ( DimCar[available_units] ),
FILTER (
ALL ( FactSale[month_within_cycle] ),
FactSale[month_within_cycle] >= MAX ( DimCar[months_available] )
)
)
And immediately we see a problem. With the fully qualified column references, it is clear that you're trying to filter the lookup table (the one side) by the base table (the many side). In Power Pivot for Excel, we do not have bi-directional relationships (though they're available in Power BI and coming for Excel 2016). Our relationships' filter context only flows from the lookup table to the base table, typically from the dimension to the fact.
Additionally, your DimCar, by holding [available_units] and [months_available] encodes an implicit assumption that a specific [car_id] can only ever refer to a single, unchanging lot. You will never see another row with [car_id] = 1. This strikes me as highly unlikely. Even if it is the case the better solution is a model change.
In general, anything that goes onto a row or column label should come from a dimension, not a fact. Similarly, anything you're aggregating in a measure should live in a fact table. The usual exception is dimension counts - either bare, or as a denominator in a measure. Following these will get you 80% of the way in terms of dimensional modeling. You can see the tables and model diagram I've ended up with in the image below.
Here are the measure definitions
SumAvailableUnits:=SUM( FactAvailability[available_units] )
SumSold:=SUM( FactSale[cars_sold] )
Here are my source tables, my model diagram with relationships, and a pivot table built from these pieces and the measures above. Note the source of [month_within_cycle] in the pivot.
Finally, you might notice that my grand total behaves in a different way than in your original. Since the values are repeated monthly, we get a much larger grand total. If you need to instead end with the sum from the latest month (which it looks like you have in your sample), you can use an alternate measure, provided below. I don't understand why this would be your desired grand total, but you can achieve it fairly easily. Personally, I'd probably blank the measure at the grand total level.
SumAvailableUnits - GrandTotal:=
SUMX(
TOPN(
1
,FactAvailability
,FactAvailability[month_within_cycle]
,0
)
,FactAvailability[available_units]
)
This uses SUMX() to step through the table provided, defined by TOPN(). TOPN() returns the first row (in this case) in FactAvailability, including ties, after sorting by [month_within_cycle], out of all rows available in the filter context. In the context of a specific month, you get all the rows associated with that month - identical to the simple sum. In the grand total context, you get the rows associated with the last month.
SUMX() iterates over that table and accumulates the values of [available_units] in a sum.