Receivables Open Items Revaluation Report

Receivables◆ Seeded · Receivables balances

Calculates the adjustment needed to restate open foreign-currency items to a revaluation rate, showing open original amount, conversion rate, open ledger amount, revaluation rate, and revalued amount by period and balancing segment.

Sample build of the Receivables Open Items Revaluation Report — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

Sample build · illustrative

Filters

Period

FEB-26

Ledger

US Primary

Currency

USD

640

Open FX items

-$210K

Revaluation adj

Currencies

Customer	Currency	Open Original	Reval Rate	Revalued	Adjustment
Acme Industrial	USD	Sample	Sample	Sample	Sample
Northwind Trading	USD	—	—	—	—
Globex Holdings	USD	Sample	Sample	Sample	Sample
Initech LLC	USD	—	—	—	—
Umbrella Corp	USD	Sample	Sample	Sample	Sample
Acme Industrial	USD	Sample	Sample	Sample	Sample

AI Analyst · active

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The report calculates the adjustment to restate open foreign-currency items to a revaluation rate.

flag

A -$210K adjustment is needed, concentrated near period end — unrealized FX on open AR that the close must book.

root cause & next step

Post the revaluation adjustment as part of close; open FX items left unrevalued misstate AR and the FX result.

Illustrative data. The live interactive version — drill-through, filters, export, and the AI Analyst — runs on your warehouse. See it live →

This is the report's BI Publisher data model — the SQL data set BI Publisher runs against Oracle tables to produce the output. The same SQL becomes a dbt model in your warehouse, so one definition drives both the formatted report and the analytics layer.

Data sources

How it interconnects: this data set reads the physical tables above. Those same tables surface in OTBI as subject areas and in BICC as PVOs — three lenses on one source. Open any table to trace its subject areas and View Objects.

The SQL data set is authored to this report's exact spec during the build and ships as the BI Publisher data model plus a matching dbt model — one definition, both layers.

The data-warehouse model — one fact surrounded by conformed dimensions (what you slice by) and measures (what you aggregate), expressed as dbt so it migrates with you. Grain: one row per source transaction.

●— fact → dimension join

Element	Type	Definition
RA_CUSTOMER_TRX_ALL	dimension	dimension
GL_DAILY_RATES	dimension	dimension
Amount	measure	measure

Runs on your cloud warehouse — Snowflake, BigQuery, Redshift, or Synapse on AWS, Google Cloud, Azure, or any provider. Reconciled to the source control total — 0% variance by design. You own the code, the model, and the data.

How the data gets here: a BICC bulk extract of the source tables above, on the same pattern for every report. See the extraction pattern & data flow →

See the complete model

How this report's fact and dimensions fit the full picture, via conformed keys.

Receivables data model →Enterprise model →

Every source object behind this report. Each linked table has its own page with full column descriptions, drawn from the Oracle BICC lineage and articulated for practitioners.

Table	Reporting columns	Subject areas
AR_PAYMENT_SCHEDULES_ALL	32	6
RA_CUSTOMER_TRX_ALL	58	16
GL_DAILY_RATES	1	10

Reporting columns = fields the report selects that are exposed as analytics attributes; subject areas = the OTBI subject areas the table appears in. Setup and configuration tables (master data, ledger and book setup, lookups) are referenced by the report's joins but aren't exposed as analytics columns or subject areas — that's expected, not a gap.

Customization note Replaced in most builds by an automated period-end revaluation that posts the unrealized FX journal directly. Irvine rebuilds these on your data.