Spreading item and location results with forecast engine Spread Measure defined and enabled

The spreading method is based on the Spreading Measure for period constant results forecast engine's parameters and Spreading Measure for time-phased results parameters, provided the parameters are defined and enabled for the current cycle.

The time-passed forecast engine results of the cycle periods (history horizon or future horizon) are prorated using the Spreading Measure for time-phased results parameter. For example, Statistical Forecast DPLS_FSTAT_EXT.

The factors used in the proration is the value for each base-level child of the selected items, location and periods divided by the value for each aggregate item, location and period with a forecast value to be prorated.

For example, The forecast engine call produces forecasts for the aggregated item node 'Car', which contains five base-level elements containing a value for the specified spreading measure (1000, 1001, 1002, 1003 and 1004). The forecast is generated at a period level of Months, wherein the calendar level to store the scenario value is Weeks. The ratio of item 1000 and location CONTINENTAL, for a specific week FY16 W18 = (Value of DPLS_FSTAT_EXT for (1000, CONTINENTAL, FY16 W18)) / (Value of DPLS_FSTAT_EXT for Car, CONTINENTAL, FY16 M05)

Note: The FY16 M05 is the month containing FY16 W18.
Note: 
  • The value of DPLS_FSTAT_EXT aggregate node must be derived from the measure values of the base-level items.
  • The spreading values from the base-level elements are used to store scenario values irrespective of the number of levels that exist between the selected Forecast Engine's level and the base-level.

When prorating, these time-phased measures are interpolated using the Spreading Measure for time phased result parameter:

  • Forecast
  • Model fitting History
  • Online Model Fit
  • Retrospective Model Fit
  • Seasonal Indices
  • Outliers
  • Step Change Exceptions
  • Tracking Signal Exceptions
  • Replacement History (only applicable when Engine type is History Replacement)
Note: For forecast engines, the exception measures (outliers, Step Change Exceptions, and Tracking Signal Exceptions) are time-dependent. Each period with an exception is set to 1. If applicable, the values are prorated to the base level to store scenario values. You can view the data at the forecasted (group or base) item, location and period level.
The time-independent Forecast Engine result values at PCONST are prorated using the Spreading Measure for period constant results parameter. For example, Valid Combinations TUPLE_EXISTS parameter. The spreading factor is based on the PCONST value for each base-level child of the selected item and location combination divided by the PCONST value for each item and location level with a forecast value to be prorated.
Note: For each item selection, in the forecast engine call, spreading values from base-level children are used to define the scenario values irrespective of the number of levels between the selected item and the Cycle.item level.
For both forecast engines, the exception measures are time dependent. Each period with an exception is set to 1. The values are stored at PCONST and are prorated using the Spreading Measure for time-phased results parameter. You can view the data at the forecasted item and location level.
Note: The same process can be used for spreading results to base location when the specified location level does not match the lowest level of the location hierarchy for the Cycle.Module.