Aggregational Behavior of Non-Cumulative Values 
Aggregational Behavior of Non-Cumulative Values
The aggregational behavior determines whether, and how, key figure values are aggregated in reports using the different characteristics or characteristic values. The aggregational behavior depends on whether you are aggregating using time characteristics or other characteristics.
If you add up all the cumulative values such as "sales revenue" using all characteristics (that is, time characteristics as well), the non-cumulative value relating to the time characteristic is often taken as the average or the last value.
When using key figures with FIRST aggregation, the respective initial status of a period is calculated. With all other types of aggregations, the final status of the period is calculated.
In the following example, the difference between the FIRST aggregation and the LAST aggregation is made clear. If one considers, for example, the aggregated values for 02.02.02, then the non-cumulative is considered 90 with the FIRST aggregation, which is the non-cumulative without receipts. The non-cumulative with the LAST aggregation is considered 110, which is the non-cumulative from 90 plus the receipts of 20.
There are two possible kinds of aggregational behavior for non-cumulative values:
For non-cumulative values such as warehouse stock, you often want to calculate the sum of the warehouse stock using different articles and different stock. However, you want to use the fiscal year and calendar periods to calculate the average value. Exception aggregation, therefore, is used for non-cumulative values with regard to time.
Exception Aggregations with Regard to Time
Every key figure has a standard aggregation and an exception aggregation. Non-cumulative values always have summation as standard aggregation, whereas time characteristics have an exception aggregation of not equal to summation.
The non-cumulative value ‘Warehouse Stock’ is aggregated using ‘Summation’ for characteristics that are not time-related such as ‘Articles’ or ‘Stock’. For time characteristics such as ‘Calendar Month’, however, the non-cumulative value ‘Warehouse Stock’ has the exception aggregation ‘Average’.
Meaningful aggregations for non-cumulative values are primarily "Average Weighted According to Calendar Days" (AV1) and "Last Value" (LAS). Additional, possible exception aggregations for non-cumulative values are listed in the following table.
Exception Aggregations for Non-Cumulative Values
|
Technical name |
Description |
|
AV1 |
Average (weighted with the number of calendar days) |
|
AV2 |
Average (weighted with the number of working days according to the factory calendar with the ID 01) |
|
FIR |
First value |
|
LAS |
Last value |
|
MAX |
Maximum |
|
MIN |
Minimum |
The time at which non-cumulative values were posted for different materials is displayed in the following graphic. The evaluation results for the non-cumulative value for ‘Material 1’, for exception aggregation "Average", and the exception aggregation "Last Value", are listed in the following tables, where they are displayed once by calendar month and once by calendar day.
In this example, we assume that validity-determining characteristics are not necessary. Therefore, the resulting validity interval is determined from the minimum and maximum postings.
This means that the validity area here is the time interval from 12.31.1999 to 03.10.2000, since the first posting was made on the 12.31.1999 and the last non-cumulative change for one of the materials (material 2) was posted on the 03.10.2000.
Drilldown on the Non-Cumulative Value for Material 1 by Calendar Month
|
Average (Calendar Day) |
Last Value |
|
|
January |
100 |
110 |
|
February |
140 |
160 |
|
March |
150 |
140 |
Drilldown on the Non-Cumulative Value for Material 1 by Calendar Day*
|
Average (Calendar Day) |
Last Value |
|
|
01.01.2000 |
90 |
90 |
|
02.01.2000 |
90 |
90 |
|
03.01.2000 |
90 |
90 |
|
04.01.2000 |
90 |
90 |
|
... |
... |
... |
|
09.01.2000 |
90 |
90 |
|
10.01.2000 |
90 |
90 |
|
11.1.2000 |
99 |
99 |
|
... |
... |
... |
*Note that non-cumulatives that are evaluated by calendar day, both for the average and for the last value, always produce the same result. The reason for this is that Calendar Day is the smallest unit of time to which the data is transferred. This always occurs when you drilldown on the most detailed time characteristic.