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SAP BusinessObjects Cloud simplifies allocation and disaggregation


In November 2015, SAP released SAP BusinessObjects Cloud, a game changing public cloud offering combining planning and analytics in a single, intuitive and powerful solution. SAP BusinessObjects Cloud has core features that form the baseline functionality and SAP BusinessObjects Cloud for Planning and SAP BusinessObjects Cloud for BI provide additional functionality for planning and BI respectively.  In this blog we will look at some features of SAP BusinessObjects Cloud for Planning that allow us to allocation and disaggregation of plan data.

SAP BusinessObjects Cloud offers three different options for disaggregation of plan data. These include Spreading, Distribution and Allocation. Here is the ‘when’ and ‘how’ of each of these options.

  • Spreading: Let us assume that we have plan data as shown in Fig 1 below where we have sales data for various regions. Let us also assume that we are currently working on a what-if scenario in our private version called ‘Myforecast’ as shown in column E below.

Fig. 1: Plan data to be distributed


We can see that we have a sales forecast of $10 Million for United States region. Also note that the region dimension has United States as the parent node and East US, West US and Overseas as nodes below the parent node. East US further has Southeast and Northeast as child nodes. Similarly, West US has Midwest and Pacific whereas Overseas region has EMEA and Asia Pacific as child nodes respectively.

The disaggregation options can be accessed as highlighted in Fig. 2 below. Spreading can be used to spread the values from parent node to child nodes. In this case since we are spreading the data from United States to East US, Midwest and Overseas.

Fig. 2 : Disaggregation options


After selecting the ‘spreading’ option, the following dialog box opens up where you can enter weights that can be used for spreading. Please note that the option to show unbooked members also is available. This becomes important if we don’t have any data for certain child nodes. For example, if there was a node ‘Alaska and Hawaii’ as a child node under United States in the region dimension but there was no forecast data entered for that region, we would have had the option to enter weight for Alaska and Hawaii in the dialog box below by switching ‘show unbooked members’ to ‘on’. Also please note that as we enter the weights, the corresponding percentage and preview after spreading is also shown in Fig 3 below.

Fig. 3: Weights for Spreading


The spreading can be applied to the data that we are seeing in the Fig 1 above. However that data view is obtained after applying certain filters to provide us our point of view. For example, the data shown in Fig 1 can be filtered for the quarters or months for which we are trying to enter forecast. However we have the option to apply the spreading either for the current point of view or some other filter values. That confirmation can be done in the next dialog box shown in Fig 4 below. If we select ‘refine filters’ then we can selected the filter values for which the spreading weights can be applied.

Fig. 4 : Filters for data input


The result after spreading is shown in Fig 5 below. Please note that though the plan data for East US, West US and Overseas is exactly what we saw in the preview in Fig 3, the values disaggregated to the next level children (Southeast, Notheast, Midwest etc.) are in the same proportion as per the original data in Fig 1. Hence we can see that spreading has been applied to the children one level below the parent node only.

Fig. 5: Result after spreading


  • Distribution: Spreading helps us to disaggregate plan data from parent node to child nodes whereas disaggregate plan data to siblings. In our example above, let us assume that we have to distribute say 1.5 Million from East US to West US and Midwest in some user specified proportion. In order to do so, we can first select the cell for East US that has the value of 4 Million and then select the disaggregation option ‘distribute’. We will get the dialog box for distribute as shown in Fig. 6 below where we can first select the target dimension, (which is the ‘Region’ in our case) and then enter amounts for West US and Overseas. Similar to the dialog box for spreading, here also we have the option for entering amounts for unbooked members which may not be shown in the original data in Fig. 1. Also, we have option to traverse up and down the hierarchy to select the nodes using the step icons. Similar to spreading, here also we can see the percentage and preview values built as we enter the amounts for distribution.

Fig. 6: Dialog box for ‘Distribute’


The result of the distribution is shown in Fig 7 below. We can see that 1.5 Million from East Us have been distributed to West US and Overseas adding to the existing values they had and the new values are shown. Please note that the distributed values have been automatically disaggregated to the lower level nodes in the same proportion in the original data.

Fig. 7: Result of distribution


  • Allocation: Allocation allows us to disaggregate data based on other drivers and rules. For example, if we want to disaggregate say IT expenses of a company to different profit centers based on some driver such as number of employees served or based on # of service tickets or a combination of metrics such as number of employees, gross revenue earned,  etc. then specific allocation steps can be configured for these.  For example, let us see how we can allocate IT expenses from unassigned product to Apparel and Footwear products as shown in Fig 8 below

Fig 8:  IT expenses to be allocated from unassigned product to Apparel and Footwear


In order to do allocations, we can either create new step or load previously defined steps as shown in Fig 9.  If we choose to load steps then we are presented with previously defined allocation steps to choose from as shown in Fig 10.

Fig 9: Create/load steps

Fig 10: Load steps from previously defined steps

We can execute allocation by choosing ‘Execute allocation’ option highlighted in Fig 2. The result of the allocating IT expenses to apparel and Footwear products is shown in Fig 11 below.

Fig 11: IT expenses after executing allocation step to allocate to Apparel and footwear


Instead of loading previously defined steps, we can also create new allocation steps by defining the source, driver and target. Let us assume that we want to allocate IT expenses for Apparel and Footwear from Overseas region to East US and West Us regions based on Gross Revenue generated by these regions. We can create a new allocation rule by following simple steps shown in Fig 12 below.

Fig 12: Simple and intuitive way to create new allocation rule


We can also visually see the allocation steps and sequence as we configure and execute the allocation as shown in Fig 13 below.

Fig 13: Visual depiction of allocation steps


The result of executing this allocation steps is shown in Fig 14 and 15 below.

Fig 14: Data before executing allocation step to allocate IT expenses from Overseas region

Fig 15: Data after executing allocation step to allocate IT expenses from Overseas region to East US and West US regions


Thus we can see that SAP BusinessObjects Cloud continues to crush complexity and simplify planning. These simple yet powerful allocation and disaggregation features go a long way in helping planners to quickly come up with various what-if scenarios to address business challenges.