Inventory aging trend with S/4HANA embedded analytics? Hmm…Sounds like data warehousing snapshot requirement. Actually S/4HANA inventory data model supports key date concept (with some limitation) making it possible trend analysis.
Example below demonstrates one of possible use cases – month to month comparison showing changes across different aging ranges
This blog describes Inventory Aging Trend model which is an evolution of Inventory Aging data model from my previous blog Inventory Aging Reporting with S/4HANA Embedded Analytics. I added monthly version dimension taking the model to a new level. Calculation of inventory quantity for any particular date was not a problem, but valuation (inventory value) was an issue and limited to current valuation. Now I made use of MBEWH Material Valuation: History table which is somewhat limited as well (contains only monthly snapshots of inventory quantity and values), but it is good enough for analysis on monthly level. I will only explain delta changes. Please refer to previous blog for complete details.
I added MBEWH Material Valuation: History table data to my model
Combined historical inventory valuation data with current valuation data. Created validity data ranges along the way making it easy to select valuation data which corresponds to selected date (key date).
I also exposed current date (key date) parameter as dimension for subsequent use as cube monthly version dimension
In my cube now I have two key date (P_Date_00 and P_Date_01) parameters one for each of two monthly versions we will compare. I pass these parameters to two table function views that I union.
Both key dates are set by BW query using OLAP variables (INVAGINGDATE00 and INVAGINGDATE01). INVAGINGDATE00 is open for entry add has current date default value set by OLAP BADI. INVAGINGDATE01 is not ready for input and is offset to prior month by OLAP BADI.
OLAP variable BADI also enforces data integrity making sure that input date variable is only limited to current date or end of the month in the past for which data model can provide correct valuation information.
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