Decentralized Planning using SAP Analytics Cloud as an extension of a SAP Business Planning and Consolidation (BPC) deployment
View this video to understand how SAP Analytics Cloud (BOC) could be positioned and used as an extension of a SAP Business Planning and Consolidation standard model (SAP BPC) deployment (hybrid approach).In this scenario, we will use some of the advanced planning techniques like “Value Driver Tree” or “Predictive Forecast” to input Budget 2017 and 2018 sales figures for all Product Lines.First, we need to import the BPC data model and its associated figures to SAP Analytics Cloud, by filtering the data we need (US, 2015-2016, Actual, Sales). During the import process, we extend the data model with a Growth Rate % driver in the account dimension, that will be used in the Value Driver Tree definition and simulations.
Then, we create a SAP Analytics Cloud story based on the newly created model, to display both Sales and Growth Rate % for all Product Lines and for all years in the data model (2016 to 2020). The driver is filled-in with manual inputs (potentially by copy-pasting from an Excel spreadsheet for example).We publish our modifications to a new Budget public version.
Once completed, we need to create the Value Driver Tree itself, defining some data source nodes (Growth Rate %) and Year-over-Year nodes (Sales) for each Product Line. Year-over-Year nodes could be calculated “On Details” (meaning on the leaf members of the remaining Time dimension) or “On Aggregates” (meaning on parent members of the remaining Time dimension). This choice will obviously change the simulation results accordingly. Finally, a Union node is added to aggregate the tree branches.
Now, in order for a Value Driver Tree to potentially be consumed from SAP Digital Boardroom (for example), it should be part of a story. So we add this Value Driver Tree tile to our existing story, and we use it to simulate our 2017 and 2018 budget figures.
Once done, 2019 figures are also generated using the Predictive Forecast capability. The predictive algorithm runs on all historical data in the data model by default, based on the defined cut over date.
Finally, all those modifications are stored into a new final budget version that we will use in the export process to BPC.
Enjoy the video 🙂