What is Cash Flow Forecasting - 13-Week Rolling Forecast?

Cash Flow Forecasting - 13-Week Rolling Forecast is an ERP workflow guide for Cash Management, Treasury & Banking. It focuses on Treasury / Cash Forecasting.

What should a finance team verify before implementation?

Given AR/AP invoice data, payroll schedules, PO commitments, and configurable collection probability curves; when a 13-week forecast is generated; then each cash flow item is bucketed into weekly periods with daily drill-down for weeks 1 - 2, manual overrides are logged with reason and analyst identity, and three scenarios (best-case, base, stress) are produced; (negative) when the forecast engine is run with a missing collection probability curve for a customer tier then the request is rejected with 422 MISSING_COLLECTION_CURVE.

Cash Flow Forecasting - 13-Week Rolling Forecast

Treasury / Cash Forecasting is where ERP discipline either begins or breaks.

Cash Flow Forecasting - 13-Week Rolling Forecast looks operational from far away. In a real finance team, it is a chain of assertions: the right actor started the work, the required records existed, the control policy was applied, the state change was preserved, and the outcome can be explained later without rebuilding the transaction from emails and spreadsheets.

The expected business outcome is specific: Treasury has a published weekly cash forecast with <10% weekly variance versus actuals, enabling proactive liquidity management.

The control flow a finance team actually needs.

Workflow map for this ERP process, including exception handling and audit evidence.

Step 1

Pull AR And AP Data As Of The Forecast...

Step 2

≥13 Weekly Time Buckets With Daily...

Step 3

Apply Configurable Collection...

Step 4

Include Committed But Unpaid Purchase...

Step 5

Manual Override With Override Reason...

The ERP surface involved.

Module

Treasury / Cash Forecasting

Actors

Treasury Analyst, FP&A Model, AR/AP Data Feeds

Tier

Tier 1

Finance area

Cash Management, Treasury & Banking

Region lens

US and UK finance teams

Publication date

March 27, 2026

Pull AR and AP data as of the forecast run date without modifying source records; support ≥13 weekly time buckets with daily drill-down for weeks 1 - 2; apply configurable collection probability curves by customer tier; include committed but unpaid purchase orders as cash outflows; support manual override with override reason logged; generate best-case, base-case, and stress-case scenarios from configurable input assumptions; export to XLSX with formulas preserved; variance report compares prior forecast to actuals with percentage deviation; forecast version history retained for 12 months.

US and UK teams have different compliance hooks, but the same control problem.

US teams usually care about clean evidence for audit support, vendor records, payment controls, tax reporting, and management review. UK teams usually care about VAT-ready records, approval evidence, digital-record discipline, and traceable postings. The country-specific details differ, but the operating pattern is the same: the ERP needs controlled records, explicit ownership, defensible state changes, and evidence that survives beyond the person who completed the task.

The control matrix.

Control area	Requirement	Acceptance proof
Control 1	Pull AR and AP data as of the forecast run date without modifying source records	Given AR/AP invoice data, payroll schedules, PO commitments, and configurable collection probability curves
Control 2	support ≥13 weekly time buckets with daily drill-down for weeks 1 - 2	when a 13-week forecast is generated
Control 3	apply configurable collection probability curves by customer tier	then each cash flow item is bucketed into weekly periods with daily drill-down for weeks 1 - 2, manual overrides are logged with reason and analyst identity, and three scenarios (best-case, base, stress) are produced
Control 4	include committed but unpaid purchase orders as cash outflows	negative) when the forecast engine is run with a missing collection probability curve for a customer tier then the request is rejected with 422 MISSING_COLLECTION_CURVE.
Control 5	support manual override with override reason logged	Treasury has a published weekly cash forecast with <10% weekly variance versus actuals, enabling proactive liquidity management.
Control 6	generate best-case, base-case, and stress-case scenarios from configurable input assumptions	Treasury has a published weekly cash forecast with <10% weekly variance versus actuals, enabling proactive liquidity management.

Audit evidence is a chain, not a folder.

Evidence layer	What should be preserved
Business event	The treasury analyst triggers a 13-week forecast generation. The system pulls scheduled AP payment runs, outstanding AR invoice due dates (weighted by historical DSO by customer segment), payroll disbursement dates from the payroll module, debt service schedules, and committed capital expenditure POs. Each cash flow item is categorized (operating, financing, investing) and assigned a weekly bucket. The model applies historical payment timing adjustments (e.g., 72% of AR due this week collected within 3 days). The analyst reviews the forecast, overrides individual line items, and publishes it. The forecast is compared to actuals weekly with variance commentary.
Control rules	Pull AR and AP data as of the forecast run date without modifying source records; support ≥13 weekly time buckets with daily drill-down for weeks 1 - 2; apply configurable collection probability curves by customer tier; include committed but unpaid purchase orders as cash outflows; support manual override with override reason logged; generate best-case, base-case, and stress-case scenarios from configurable input assumptions; export to XLSX with formulas preserved; variance report compares prior forecast to actuals with percentage deviation; forecast version history retained for 12 months.
Acceptance proof	Given AR/AP invoice data, payroll schedules, PO commitments, and configurable collection probability curves; when a 13-week forecast is generated; then each cash flow item is bucketed into weekly periods with daily drill-down for weeks 1 - 2, manual overrides are logged with reason and analyst identity, and three scenarios (best-case, base, stress) are produced; (negative) when the forecast engine is run with a missing collection probability curve for a customer tier then the request is rejected with 422 MISSING_COLLECTION_CURVE.
Data record	`cash_forecast { forecast_id: string, entity_id: string, run_date: date, scenario: enum(BASE, BEST, STRESS), version: int, status: enum(DRAFT, PUBLISHED), generated_at: timestamp, external_id: string }; forecast_line { line_id: string, forecast_id: string, week_bucket: int, day_date: date, category: enum(OPERATING, INVESTING, FINANCING), amount_minor: int64, currency_code: char(3), is_override: bool, override_reason: string }; (reference, product may differ).`
System event	`POST /v1/treasury/forecasts { entity_id, run_date, scenarios: [string] } -> 202 { forecast_id }; GET /v1/treasury/forecasts/{id}/lines { week_bucket, category }; PATCH /v1/treasury/forecasts/{id}/lines/{line_id} { amount_minor, override_reason }; emits treasury.forecast_published event; idempotent via external_id.`
Lifecycle state	`DRAFT -> PUBLISHED; terminal ARCHIVED; guard: PUBLISHED requires all mandatory category lines populated; prior forecast versions retained for 12 months.`

The useful version of this workflow is not only fast. It is inspectable. A controller, auditor, or operator should be able to move from source event to system record to state transition to final business outcome without guessing.

Implementation contracts.

Reference data model

`cash_forecast` { forecast_id: string, entity_id: string, run_date: date, scenario: enum(BASE, BEST, STRESS), version: int, status: enum(DRAFT, PUBLISHED), generated_at: timestamp, external_id: string }; `forecast_line` { line_id: string, forecast_id: string, week_bucket: int, day_date: date, category: enum(OPERATING, INVESTING, FINANCING), amount_minor: int64, currency_code: char(3), is_override: bool, override_reason: string }; (reference, product may differ).

API and events

`POST /v1/treasury/forecasts` { entity_id, run_date, scenarios: [string] } -> 202 { forecast_id }; `GET /v1/treasury/forecasts/{id}/lines` { week_bucket, category }; `PATCH /v1/treasury/forecasts/{id}/lines/{line_id}` { amount_minor, override_reason }; emits `treasury.forecast_published` event; idempotent via external_id.

State transitions

`DRAFT -> PUBLISHED`; terminal `ARCHIVED`; guard: PUBLISHED requires all mandatory category lines populated; prior forecast versions retained for 12 months.

Common implementation traps.

Treating the workflow as data entry

If the ERP only stores the final record, the team loses the decision trail that explains how the record became valid.

Hiding exception logic

Exceptions need owners, reason codes, and time stamps. A vague pending state is not a control.

Posting without recovery design

Retries, duplicate submissions, and partial failures must be explicit so the system does not create inconsistent records.

Skipping evidence design

A workflow that cannot produce evidence on demand will eventually push finance teams back into manual screenshots and spreadsheets.

Where Rivane fits.

Rivane is built for finance workflows where automation must stay tied to source documents, approvals, state transitions, ledger impact, reporting, and audit evidence. Use this guide as a checklist for evaluating whether an ERP workflow is merely digitized or actually controlled.

References and source basis.

These sources provide the standards, regulatory, or government context around the flow. They are included so the guide is useful to finance operators, auditors, and implementation teams, not only buyers reading software copy.

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