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EBS Reference

MRP_FORECAST_DATES

Schema: MRPtransactionPer inventory org

The forecast fact — one row per forecast entry per item, org, forecast name, and date, carrying both the entered quantity and the post-consumption remainder

Module: PlanningInventory-org partitioned (ORGANIZATION_ID)
Grain note

A single row spans MANY buckets when RATE_END_DATE is set — expand before summing; and CURRENT_FORECAST_QUANTITY is post-consumption, so summing ORIGINAL double-counts demand already consumed by sales orders

Notes

Original minus current is what sales orders consumed. Bucket types are daily, weekly, and periodic — the bucket decides which dates are legal.

What the badges mean
master
Data class: what the table holds — master data, transaction documents, control/configuration, interface/staging, or an APPS-schema view.
In field listings, K marks a primary-key field.

Fields

15 fields · 1 key

Table fields: position, field name, description, data type, and flags. 15 fields.
#FieldDescriptionTypeFlags
1TRANSACTION_IDSurrogate key of the forecast entryNUMBER
Key
2INVENTORY_ITEM_IDThe forecast itemNUMBER
3ORGANIZATION_IDInventory organizationNUMBER
4FORECAST_DESIGNATORThe forecast the entry belongs to — joins with the orgVARCHAR2
5FORECAST_DATEThe bucket date — the analysis dateDATE
Filter date
6RATE_END_DATENULL for a single bucket — when set, the row SPANS buckets from the forecast date to here; expand before summingDATE
7BUCKET_TYPEDaily, weekly, or periodic bucketNUMBER
8ORIGINAL_FORECAST_QUANTITYThe entered quantity, before consumptionNUMBER
9CURRENT_FORECAST_QUANTITYThe post-consumption remainder — original minus current is what sales orders consumedNUMBER
10CONFIDENCE_PERCENTAGEConfidence attached to the entryNUMBER
11ORIGINATION_TYPEHow the entry was created — manual, load, copy…; numeric, undecodedNUMBER
12SOURCE_FORECAST_DESIGNATORThe forecast this entry was copied/loaded fromVARCHAR2
13SOURCE_ORGANIZATION_IDSource org of a copied entryNUMBER
14END_ITEM_IDThe planning-bill parent this entry was exploded fromNUMBER
15FORECAST_RULE_IDThe forecast rule that generated statistical entriesNUMBER

Field provenance: hand-curated. 1 key field.

Boilerplate SQL

Starting point for reading MRP_FORECAST_DATESon Databricks — real DATE columns need no conversion, and the org anchor and the LAST_UPDATE_DATE watermark are already in place. Set your Unity Catalog location, schema, and org values below; they’re substituted into the SQL and the copy button.

Query parameters
-- ============================================================
-- Table  : MRP_FORECAST_DATES — The forecast fact — one row per forecast entry per item, org, forecast name, and date, carrying both the entered quantity and the post-consumption remainder
-- Purpose: Column-selected read of MRP_FORECAST_DATES — auto-generated from field metadata
-- Grain  : One row per inventory org (ORGANIZATION_ID) + TRANSACTION_ID
-- Caution: A single row spans MANY buckets when RATE_END_DATE is set — expand before summing; and CURRENT_FORECAST_QUANTITY is post-consumption, so summing ORIGINAL double-counts demand already consumed by sales orders
-- Notes  : Auto-generated skeleton for Oracle EBS R12 data landed in your lakehouse. Dates are real DATE/TIMESTAMP columns — no conversion needed. WHO audit columns omitted (see the quirks guide); the optional LAST_UPDATE_DATE watermark filter supports incremental extracts.
-- ============================================================
SELECT
  t.TRANSACTION_ID AS "Surrogate key of the forecast entry",
  t.INVENTORY_ITEM_ID AS "The forecast item",
  t.ORGANIZATION_ID AS "Inventory organization",
  t.FORECAST_DESIGNATOR AS "The forecast the entry belongs to — joins with the org",
  t.FORECAST_DATE AS "The bucket date — the analysis date",
  t.RATE_END_DATE AS "NULL for a single bucket — when set, the row SPANS buckets from the forecast date to here; expand before summing",
  t.BUCKET_TYPE AS "Daily, weekly, or periodic bucket",
  t.ORIGINAL_FORECAST_QUANTITY AS "The entered quantity, before consumption",
  t.CURRENT_FORECAST_QUANTITY AS "The post-consumption remainder — original minus current is what sales orders consumed",
  t.CONFIDENCE_PERCENTAGE AS "Confidence attached to the entry",
  t.ORIGINATION_TYPE AS "How the entry was created — manual, load, copy…; numeric, undecoded",
  t.SOURCE_FORECAST_DESIGNATOR AS "The forecast this entry was copied/loaded from",
  t.SOURCE_ORGANIZATION_ID AS "Source org of a copied entry",
  t.END_ITEM_ID AS "The planning-bill parent this entry was exploded from",
  t.FORECAST_RULE_ID AS "The forecast rule that generated statistical entries"
FROM <catalog>.<schema>.MRP_FORECAST_DATES t
WHERE
  t.ORGANIZATION_ID = <inventory_org_id>  -- inventory org, NOT the operating unit — see quirks guide #two-orgs
  -- AND t.TRANSACTION_ID = <TRANSACTION_ID>
  -- AND t.FORECAST_DATE >= DATE '<DATE_FROM>'
  -- AND t.FORECAST_DATE <= DATE '<DATE_TO>'
  -- AND t.LAST_UPDATE_DATE >= TIMESTAMP '<watermark>'  -- WHO watermark, bulk-stamped by batch jobs; see quirks guide #who-columns
ORDER BY t.TRANSACTION_ID;

7 parameters not filled: <catalog>, <schema>, <inventory_org_id>, <TRANSACTION_ID>, <DATE_FROM>, <DATE_TO>, <watermark>

Relationships

1-hop neighbors — click a table to navigate there. FND lookup decode and translation edges are highlighted; they’re the joins newcomers most often get wrong.

Join details

  • MRP_FORECAST_DATESMRP_FORECAST_DESIGNATORSforeign key · N:1
    ON MRP_FORECAST_DATES.FORECAST_DESIGNATOR = MRP_FORECAST_DESIGNATORS.FORECAST_DESIGNATOR AND MRP_FORECAST_DATES.ORGANIZATION_ID = MRP_FORECAST_DESIGNATORS.ORGANIZATION_ID
  • MRP_FORECAST_DATESMTL_SYSTEM_ITEMS_Bforeign key · N:1
    ON MRP_FORECAST_DATES.INVENTORY_ITEM_ID = MTL_SYSTEM_ITEMS_B.INVENTORY_ITEM_ID AND MRP_FORECAST_DATES.ORGANIZATION_ID = MTL_SYSTEM_ITEMS_B.ORGANIZATION_ID
  • MRP_SCHEDULE_DATESMRP_FORECAST_DATESforeign key · N:1
    ON MRP_SCHEDULE_DATES.FORECAST_ID = MRP_FORECAST_DATES.TRANSACTION_ID AND MRP_SCHEDULE_DATES.ORGANIZATION_ID = MRP_FORECAST_DATES.ORGANIZATION_ID

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