Consolidation and Intercompany Elimination with parallel hierarchies

In my last post I described and approach on how to calculate consolidated values (adjusted by intercompany eliminations) using PowerPivot and DAX. In this post I will extend this technique so it can also be used with parallel hierarchies as this is also very common in financial applications.

Switching from a single hierarchy to multiple parallel hierarchies has several drawbacks for tabular models:
1) business keys cannot be used to build the PC-hierarchy as they are not unique anymore
2) artificial keys and parent-keys are required
3) as business keys are not unique anymore, we also cannot create a relationship between facts and our dimension
4) PowerPivot tables do not support "UNION" to load several single hierarchies and combine them

All the issues described above have to be handled before the hierarchies can be loaded into PowerPivot. This is usually done in a relational database but this will not be covered in this post.

For simplicity I created a table in Excel that already has the correct format:

ID ParentID SenderID Name
1   TOT My Whole Company by Region
2 1 EUROPE Europe
3 2 GER Germany Company
4 2 FR French Company
5 1 NA North America
6 5 USA US Company
7 5 CAN Canadian Company
-99   EXTERNAL EXTERNAL
8   TOT_2 My Whole Company Legal View
9 8 JV Joint Ventures
10 9 FR French Company
11 9 USA US Company
12 8 HOLD My cool Holding
13 12 GER Germany Company
14 12 CAN Canadian Company

We have 2 parallel hierarchies – "My Whole Company by Region" and "My Whole Company Legal View". In PowerPivot this looks like this:

FullHierarchy

Each Company can be assigned to different nodes in every hierarchy.

As I already noted above, the SenderIDs are not unique anymore, and therefore we cannot create a relationship to our fact-table. So we have to handle this in our calculation. The approach is similar to handling SCD2 dimensions in PowerPivot what I described in previous posts (Part1 and Part2). We use CONTAINS-function to filter our fact-table based on the currently active SenderIDs in the dimension-table:

Value_Sender:=
CALCULATE([Value_SUM],
FILTER(
     'Facts',
     CONTAINS(
          'Sender',
          'Sender'[SenderID],
          'Facts'[SenderID]))
)

Actually this is the only difference in the calculation when we are dealing with multiple parallel hierarches. The other calculations are the same with the only difference that we have to use [Value_Sender] instead of [Value_SUM]:

Value_Internal:=
CALCULATE([Value_Sender],
FILTER(
     'Facts',
     CONTAINS(
          'Sender',
          'Sender'[SenderID],
          'Facts'[ReceiverID]))
)

and

Value_External:=
CALCULATE([Value_Sender],
FILTER(
     'Facts',
     NOT(
          CONTAINS(
               'Sender',
               'Sender'[SenderID],
               'Facts'[ReceiverID])))
)

As you can see this approach also works very well with multiple hierarchies. In the end we get the desired results:

FinalResults

Taking a look at "My cool Holding" we see that a value of 50 is eliminated as there have been sales from CAN to GER. On top-level of the hierarchy we see the same values being eliminated (170) as both hierarchies contain the same companies and therefore only sales to EXTERNAL can summed up to get the correct value.

As this technique operates on leaf-level it works with all possible hierarchies regardless of how many companies are assigned to which node or how deep the hierarchies are!

 

(Just to compare the values from above – the fact-table is the same as in the previous post: )

SenderID ReceiverID Value
FR GER 100
CAN GER 50
GER EXTERNAL 70
USA CAN 30
USA FR 10
USA EXTERNAL 90
CAN USA 50
CAN EXTERNAL 10

Consolidation and Intercompany Elimination made easy using PowerPivot and DAX

In my past years as a consultant for Microsoft BI I had to deal with financial models quite a lot. They are usually small in size but very complex in terms of calculations like currency conversion, margin-calculations, benchmarks and so on. Another topic I came across very frequently was consolidation and intercompany eliminations. Recently I had to deal with this topic again and thought of how this could be solved in PowerPivot/DAX.

First of all a little background information on what consolidation and intercompany eliminations are. In bigger companies with several subsidiaries those subsidiaries are usually structured in some way – e.g. by legal entity, by country etc..

Here is a little example of a corporate group called "My Whole Company" with subsidiaries in Germany, France, USA and Canada, structured by geography:

SampleHierarchy

These companies also make business with each other and also with external customers. The French Company and the Canadian Company sell something to the Germany Company, US Company sells goods to the Canadian Company and French Company and so on.

To get a consolidated value for higher nodes (e.g. Europe), transactions between companies within Europe have to be eliminated. Otherwise sales within Europe would be aggregated up even though the goods never left Europe and therefor cannot be added to Europe’s sales.

(I am not a financial guy so I focused on the technical stuff rather then on the business backgrounds. This whole example just demonstrates a general approach to solve this issue technically.)

 

Our fact-table contains columns for the SenderCompany, ReceiverCompany and Value:

SenderID ReceiverID Value
FR GER 100
CAN GER 50
GER EXTERNAL 70
USA CAN 30
USA FR 10
USA EXTERNAL 90
CAN USA 50
CAN EXTERNAL 10

For our Sender and Receiver-Table we can use the same source-table (details on this follow):

SenderID ParentSenderID Name
TOT   My Whole Company
EUROPE TOT Europe
GER EUROPE Germany Company
FR EUROPE French Company
NA TOT North America
USA NA US Company
CAN NA Canadian Company
EXTERNAL   EXTERNAL

Note that we use a parent-child hierarchy here as most financial applications use them to represent hierarchical structures. The parent-child hierarchy is resolved using PATH(), PATHITEM() and LOOKUPVALUE() functions as described here by Alberto Ferrari. The final table is added twice to our model, for Sender and Receiver:

PowerPivotModel

(for simplicity I removed all other columns of the fact-table and their corresponding dimension-tables).

The next step is to implement the business logic for consolidation and intercompany elimination. From the description above we can break this logic down into smaller pieces. To get a consolidated value, we have to:

  1. Select only Senders below the currently selected SenderHierarchy-Node
  2. Select only Receivers, that are NOT below the ReceiverHierarchy-Node that is equal to the currently selected SenderHierarchy-Node
  3. Sum up the remaining fact-rows
    Step 1 is done automatically for us because we defined a relationship between our Sender- and our Facts-table.
    Step 3 is easily done with the following DAX calculation combined with the right filters (defined in Step 2 below):
    Value_SUM:=SUM(Facts[Value])
    Step 2 is the tricky one.
    If you think of multidimensional models this is usually solved by a combination of AGGREGATE() and LINKMEMBER(). LINKMEMBER() is used to select the same node in the ReceiverHierarchy that is currently selected in the SenderHierarchy and AGGREGATE() is then used to aggregate all except the leaves below that node. This complex calculation may result in bad performance and is also not easily readable. Though there are also other implementations using many-to-many relationships and so on that perform better, but also make the model even more complex.
    Fortunately there is DAX  that can deal with this problem very easily as we will see now. All of the following calculations use the CONTAINS-function. It can be used to filter a table based on an other column’s values, similar to the IN-operator in T-SQL. First of all we will start with an example where we calculate all sales within a selected node. If the node "Europe" is selected, we want to see all sales within "Europe", meaning where Sender and Receiver are below the node "Europe":
    Value_Internal:=
    CALCULATE(
         [Value_SUM],
         FILTER(
              'Receiver',
              CONTAINS(
                   'Sender', 
                   'Sender'[SenderID], 
                   'Receiver'[ReceiverID])))

    The Receiver-table is filtered using the CONTAINS-function. As FILTER operates row-by-row, each ‘Receiver’[ReceiverID] is checked whether its value can also be found in the currently selected SenderIDs. In T-SQL this would be similar to:

    SELECT SUM([Value]
    FROM Facts
    INNER JOIN Sender
        ON Facts.SenderID = Sender.SenderID
    INNER JOIN Receiver
        ON Facts.ReceiverID = Receiver.ReceiverID
    WHERE Receiver.ReceiverID IN (SELECT SenderID 
                                  FROM Sender 
                                  WHERE Level2='Europe')
    AND Sender.Level2 = 'Europe'

    Once you understand how the T-SQL, the DAX-calculation should also be clear.

      This was the calculation we can use to calculate our internal sales. To calculate the consolidated sales for our "Europe"-node we have to do the opposite – select only Receivers that are NOT below the currently selected Sender-node. In T-SQL we would simply use "NOT IN" – in DAX this is similar. All we have to do is to wrap a NOT() around our CONTAINS-function:
      Value_External:=
      CALCULATE(
           [Value_SUM],
           FILTER(
                'Receiver',
                NOT(
                     CONTAINS(
                          'Sender', 
                          'Sender'[SenderID], 
                          'Receiver'[ReceiverID]))))

      And this is already our final calculation to eliminate intercompany sales and get a consolidated value for any Sender-node we select!

      ConsolidatedValues1

    Europe has total sales of 170 of which 100 have been internal (FR to GER) and 70 have been external (GER to EXTERNAL). North America has a total value of 240 of which 80 have been internal (USA to CAN 30 and CAN to USA 50) and 160 have been sold external to either Europe or external customers.

     

    The calculation can be further simplified in terms of usability. You may already realized that we did not refer to our hierarchy within any of our calculation. We always used the leaf-level (SenderID/ReceiverID) to calculated our values. To solve the original problem in a multidimensional model we had to create a Receiver-dimension and select/deselect members based on the current selection in the Sender-hierarchy. This is not necessary anymore as we only use leaf-elements in our calculation. So we do not need a Receiver-hierarchy and further also no Receiver-table in our PowerPivot-model as we already have the ReceiverID in our Facts-table:

    Value_External_Facts:=
    CALCULATE(
         [Value_SUM]; 
         FILTER(
              'Facts'; 
              NOT(
                   CONTAINS(
                        'Sender'; 
                        'Sender'[SenderID];
                        'Facts'[ReceiverID]))))

     

    There may also be reasons that the user wants to select the Receiver-Node on its own. For example to see sales from European to external customers (EXTERNAL).  To do this you have to adopt the calculation as follows:

    Value_Selected:=CALCULATE(
         [Value_SUM];
          FILTER(
              ALL(Receiver); 
              NOT(
                   CONTAINS(
                        'Receiver'; 
                        'Receiver'[ReceiverID];
                        'Receiver'[ReceiverID]))))

    This calculation may look strange on first sight but does exactly what we need. It removes all filters from the Receiver-table and applies a new filter that is the opposite of the currently existing filter using a combination of NOT() and CONTAINS(). The 3rd parameter of the CONTAINS-function always refers to the row-context of the FILTER-function whereas the 1st and the 2nd parameter are always based on the current query-context.

    These are the results:

    ConsolidatedValues_Selected

    You could also use the Value_SUM-calculation to get the opposite value – what has been sold from the current Sender-node to any Receiver located in Europe. Our Canadian Company for example has sold 50 to GER which is below Europe and 60 to companies outside Europe – in this case 50 to USA and 10 to EXTERNAL.

     

    As you can see using DAX makes these kind of calculations very easy compared to multidimensional models. An other important aspect is that financial models are usually very small and therefor should fit into memory very easily. Also the independence of hierarchies is an important aspect here.

    You can also download the Excel-Workbook with the sample-calculations here:

     

    UPDATE:

    In Part2 I will show how to handle multiple parallel hierarchies.