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Overview
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AI-generated from documented ETRM metadata — verify critical details on the linked pages.

Overview

FII_AR_REV_SUM_V2 is a reporting view owned by the APPS schema in Oracle E-Business Suite, delivered as part of the FII (Financial Intelligence) product family. It exposes summarized accounts receivable revenue data drawn from a materialized view, providing a denormalized, query-friendly projection of revenue by time, organization, and channel dimensions. The view is commonly consulted in 12.1.1 and 12.2.2 environments where Financial Intelligence dashboards and analytical extracts require pre-aggregated AR revenue figures. Because it resolves to a materialized view rather than a live transactional table, the view offers improved query performance for trend reporting and period-over-period comparisons.

Underlying Base Objects

The ETRM metadata documents a single referenced base object: the materialized view FII_AR_REV_SUM_MV. The view text is a straightforward projection over that source:

No additional base tables are documented in the ETRM metadata, and the view presents no joins or filter predicates of its own. Consequently, all aggregation logic, refresh scheduling, and dimensional resolution occur within the underlying materialized view. The view therefore acts as a stable interface, insulating consumers from changes to the materialized view's internal structure.

Key Columns

The columns exposed by FII_AR_REV_SUM_V2 map directly to the materialized view's structure and support dimensional analysis of revenue:

  • TIME_ID — Surrogate key to the time dimension, defining the accounting period for the summarized revenue.
  • PERIOD_TYPE_ID — Identifies the period granularity (for example, month, quarter, or year) associated with the time key.
  • LINE_OF_BUSINESS_ID — Identifier for the line of business, the column most frequently targeted in user searches. It enables revenue segmentation by business unit or reporting segment.
  • MANAGER_ID — Identifier of the responsible sales or business manager, supporting accountability and performance reporting.
  • PRODUCT_CATEGORY_ID — Product category classification for the summarized revenue.
  • LEDGER_ID — Identifies the ledger set or primary ledger, aligning revenue figures to the correct accounting books.
  • SALES_CHANNEL_CODE — Code denoting the sales channel through which the revenue was recognized.
  • REV_ACTUAL_B — Actual revenue amount, typically expressed in the ledger's functional or base currency.
  • REV_SEC_ACTUAL_G — Secondary actual revenue amount, typically expressed in a global or reporting currency.

Note that the ETRM column listing also references ACTUAL_B and ACTUAL_G, which correspond to the revenue measure columns described above.

Common Use Cases and Queries

Typical usage centers on revenue analysis by line of business, manager, product category, ledger, and channel for a given period. A common pattern filters on LINE_OF_BUSINESS_ID to isolate a specific business segment, as reflected in the search that surfaced this object:

  • Revenue by line of business for a period: SELECT line_of_business_id, SUM(rev_actual_b) FROM apps.fii_ar_rev_sum_v2 WHERE time_id = :p_time_id GROUP BY line_of_business_id;
  • Manager performance within a channel: SELECT manager_id, sales_channel_code, SUM(rev_actual_b) FROM apps.fii_ar_rev_sum_v2 WHERE time_id = :p_time_id AND ledger_id = :p_ledger_id GROUP BY manager_id, sales_channel_code;
  • Functional versus reporting currency comparison: SELECT time_id, line_of_business_id, SUM(rev_actual_b), SUM(rev_sec_actual_g) FROM apps.fii_ar_rev_sum_v2 GROUP BY time_id, line_of_business_id;
  • Product category trend across periods: SELECT period_type_id, product_category_id, SUM(rev_actual_b) FROM apps.fii_ar_rev_sum_v2 WHERE line_of_business_id = :p_lob GROUP BY period_type_id, product_category_id;

Consumers should qualify queries with the APPS schema, restrict results by TIME_ID and LEDGER_ID where possible to leverage the materialized view's structure, and account for materialized view refresh timing when interpreting near-real-time figures.