Search Results mrp_bis_forecast_v




Overview
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Overview

MRP_BIS_FORECAST_V is a reporting view owned by the APPS schema in Oracle E-Business Suite (validated across 12.1.1 and 12.2.2). It belongs to the MRP (Master Scheduling/MRP) product family and is designed to expose forecast-versus-actual performance data for consumption by Oracle Business Intelligence System (BIS) dashboards and related analytical reporting. The view aggregates forecast and order quantities and monetary amounts by forecast set, forecast designator, demand class, and organization, and it derives a forecast error metric from those aggregated figures.

Because the object is a view rather than a base table, it does not store data. Instead, it presents a pre-joined, pre-grouped projection over underlying forecast trend data. This design allows reporting layers and integration programs to query a single consolidated structure instead of reconstructing the aggregation logic themselves, ensuring consistent calculation of forecast accuracy across all consumers.

Underlying Base Objects

The documented dependency chain for MRP_BIS_FORECAST_V is compact and consists of two referenced objects:

  • MRP_BIS_FORECAST_TREND_V (VIEW) — the immediate source of row-level forecast and order data. MRP_BIS_FORECAST_V selects from this trend view, applies NVL normalization, and groups the results.
  • MRP_GET_BIS_VALUES (PACKAGE) — supplies the FORECAST_ERROR function used to compute the derived error column. The view invokes MRP_GET_BIS_VALUES.FORECAST_ERROR against the summed forecast and order amounts and wraps the result in ABS and NVL to guarantee a non-negative, non-null value.

The grouping is performed on FORECAST_SET, NVL(DEMAND_CLASS, ' '), ORGANIZATION_ID, and FORECAST_DESIGNATOR, meaning each output row represents a unique combination of those four dimensions. The dependency on MRP_BIS_FORECAST_TREND_V indicates that the trend view itself encapsulates any joins to forecast, demand, and order detail sources; MRP_BIS_FORECAST_V is therefore an aggregation layer rather than a direct query against forecast tables.

Key Columns

  • FORECAST_SET — Identifies the forecast set from which the forecast entries originate; a primary grouping dimension.
  • FORECAST_DESIGNATOR — The forecast designator associated with the aggregated rows; the second grouping dimension.
  • DEMAND_CLASS — The demand class for the forecast, normalized to a single space when null via NVL(DEMAND_CLASS, ' '); included in the GROUP BY so that null demand classes form their own bucket.
  • ORGANIZATION_ID — The inventory organization identifier, normalized to 0 when null via NVL(ORGANIZATION_ID, 0).
  • FORECAST_QUANTITY — Sum of forecast quantities, normalized to 0 when null via NVL(SUM(...), 0).
  • ORDER_QUANTITY — Sum of order (actual consumption) quantities, likewise normalized to 0.
  • FORECAST_AMOUNT — Sum of forecast monetary amounts, normalized to 0.
  • ORDER_AMOUNT — Sum of order monetary amounts, normalized to 0.
  • FORECAST_ERROR — Derived metric computed as NVL(ABS(MRP_GET_BIS_VALUES.FORECAST_ERROR(SUM(FORECAST_AMOUNT), SUM(ORDER_AMOUNT))), 0). This expresses the absolute variance between forecast and actual order amounts, providing a standardized accuracy indicator.

The pervasive use of NVL ensures that reporting consumers receive zero or placeholder values rather than nulls, which simplifies summation and comparison in BI tools.

Common Use Cases and Queries

MRP_BIS_FORECAST_V is typically used in forecast accuracy and demand-planning dashboards, where analysts compare planned quantities and values against actual order consumption. A representative query grouping by organization and forecast set:

  • SELECT forecast_set, forecast_designator, demand_class, organization_id, forecast_quantity, order_quantity, forecast_amount, order_amount, forecast_error FROM apps.mrp_bis_forecast_v WHERE organization_id = :org_id ORDER BY forecast_set, forecast_designator;
  • SELECT forecast_set, SUM(forecast_amount), SUM(order_amount), SUM(forecast_error) FROM apps.mrp_bis_forecast_v GROUP BY forecast_set;

Typical scenarios include measuring forecast bias by demand class, ranking forecast sets by total error, and feeding extract programs that populate custom data warehouses. Because the view performs all aggregation and error calculation internally, consumers need only apply their own filtering and ordering predicates, avoiding duplication of the FORECAST_ERROR logic across multiple reporting artifacts.