Payables Invoice Aging Report

Payables

The Payables Invoice Aging Report buckets open invoice liability by age (current, 1–30, 31–60, 61–90, 90+) so AP can prioritize payment runs and forecast cash. It reads the open installments behind the GL liability balance.

Used by · AP managers managing payment runs and cash forecasting.Run · Weekly for payment planning; at close for liability review.

The question it answers

How much supplier liability is open, how is it aged, and which suppliers concentrate the risk?

Sample build of the Payables Invoice Aging Report — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

Payables Invoice Aging Report

Sample build · illustrative

Filters

Business Unit

APAC

As-of Date

2026-02-28

Currency

USD

Supplier

Globex Holdings

$14,902,310

Open Liability

$11,640,200

Current

$486,540

90+ Days

318

Suppliers

Supplier	Current	1-30	31-60	61-90	90+	Total Open
Acme Industrial	Sample	Sample	Sample	Sample	Sample	$1,240,500.00
Northwind Trading	—	—	—	—	—	$842,150.75
Globex Holdings	Sample	Sample	Sample	Sample	Sample	$96,400.00
Initech LLC	—	—	—	—	—	$1,005,233.10
Umbrella Corp	Sample	Sample	Sample	Sample	Sample	$58,720.40
Acme Industrial	Sample	Sample	Sample	Sample	Sample	$1,240,500.00

AI Analyst · active

reading

Each row is a supplier's open liability split into aging buckets. The Current bucket is healthy payable; the 90+ bucket is where disputes and missed discounts hide.

flag

$486,540 sits in 90+ days, concentrated in a single supplier — likely on hold rather than genuinely overdue, which distorts the cash forecast.

root cause & next step

Traced to AP_PAYMENT_SCHEDULES_ALL joined to AP_HOLDS_ALL: the balance is held on a quantity mismatch. Resolve the hold or exclude held items so the aging reflects true payable.

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
AP_PAYMENT_SCHEDULES_ALL	dimension	dimension
POZ_SUPPLIERS	dimension	dimension
Total Open	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.

Payables 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
AP_INVOICES_ALL	63	15
AP_PAYMENT_SCHEDULES_ALL	21	2
POZ_SUPPLIERS	145	75

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.