Receipts Days Late Analysis Report

Receivables◆ Seeded · Receipts

Measures the cost of slow-paying customers by computing weighted-average days late per customer, with transaction number, type, due date, receipt number, days late, and weighted days late.

Sample build of the Receipts Days Late Analysis 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

1,840

Customers

11.4

Avg days late

Worst tier > 30d

Customer	Receipts	Weighted Days Late	Amount	Cost Of Delay
Acme Industrial	Sample	Sample	$1,240,500.00	$1,240,500.00
Northwind Trading	—	—	$842,150.75	$842,150.75
Globex Holdings	Sample	Sample	$96,400.00	$96,400.00
Initech LLC	—	—	$1,005,233.10	$1,005,233.10
Umbrella Corp	Sample	Sample	$58,720.40	$58,720.40
Acme Industrial	Sample	Sample	$1,240,500.00	$1,240,500.00

AI Analyst · active

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The report computes weighted-average days late per customer to measure the cost of slow payers.

flag

42 customers average over 30 days late — a concentrated problem dragging DSO and working capital.

root cause & next step

Target collection terms or credit holds on the worst tier; weighted days-late is where real DSO improvement comes from.

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
AR_RECEIVABLE_APPLICATIONS_ALL	dimension	dimension
AR_PAYMENT_SCHEDULES_ALL	dimension	dimension
Amount	measure	measure
Cost Of Delay	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_CASH_RECEIPTS_ALL	25	9
AR_RECEIVABLE_APPLICATIONS_ALL	35	2
AR_PAYMENT_SCHEDULES_ALL	32	6

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 A strong collections signal that is usually rebuilt as a trended DSO and days-late scorecard by customer and collector. Irvine rebuilds these on your data.