Reserve Adjustments Report

Fixed Assets◆ Seeded · Adjustments

Adjustments to depreciation reserve made in a period, with the amount and the reserve accounts affected.

Sample build of the Reserve Adjustments 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

Adjustments

$620K

Net reserve change

Unexplained

Asset	Adjustment Type	Amount	Reserve Account	Date
Sample	Standard	$1,240,500.00	1000-2100-000	2026-04-30
—	Corporate	$842,150.75	1000-5400-000	2026-03-31
Sample	Standard	$96,400.00	1000-1410-000	2026-02-28
—	Default	$1,005,233.10	2000-2100-000	2026-01-31
Sample	Standard	$58,720.40	1000-6300-000	2025-12-31
Sample	Standard	$1,240,500.00	1000-2100-000	2026-04-30

AI Analyst · active

reading

The report lists reserve adjustments with amount and the accounts affected.

flag

Four reserve adjustments have no matching cost or method change — reserve moved on its own, which shouldn't happen without a basis.

root cause & next step

Tie each reserve adjustment to its driver; a standalone reserve change usually means a prior depreciation error is being corrected.

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

FA_ADJUSTMENTS
FA_DEPRN_SUMMARY
FA_BOOKS

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
FA_DEPRN_SUMMARY	dimension	dimension
FA_BOOKS	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.

Fixed Assets 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
FA_ADJUSTMENTS	8	3
FA_DEPRN_SUMMARY	Setup / configuration table — joined for reference, not exposed for analytics
FA_BOOKS	20	2

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 Tracked with cost adjustments to explain every change to net book value. Irvine rebuilds these on your data.