Cash Flow Forecasting with Your ERP Data: A Practical Guide

Building a Rolling 13-Week Cash Flow Forecast

A rolling forecast always looks 13 weeks ahead. Each week, actual figures replace the oldest week's forecast and a new week 13 is added. Step by step:

1. Fix the calendar. Use weeks that start on the same day, for example Monday, labeled by date.
2. Start from reconciled cash. Take bank and cash balances at the start of week one.
3. Add receipts. Place open invoices in the week each customer is expected to pay, then uninvoiced orders by expected delivery and terms, and other receipts such as VAT refunds.
4. Add payments. Place bills in the payment run that will pay them, then purchase orders by expected bill date and terms, payroll on its pay dates, taxes on their due dates and recurring costs on their schedules.
5. Calculate each week's closing balance and compare it with your minimum cash buffer and any undrawn facility.
6. Act on the pinch points. Where the balance falls below the buffer, decide now: chase specific invoices, move a payment run or arrange the facility.
7. Roll forward weekly. Record the variance between forecast and actual for each line, then add the new week.

Take a business that starts its forecast on Monday 17 August 2026, with a 31 December year end and quarterly VAT periods ending in September. Two weeks stand out: week seven, which contains 30 September and the corporate tax for the 2025 financial year, and week eleven, which contains 28 October and the VAT for July to September. If month-end payroll and a large payment run fall in the same weeks, those are the weeks to plan first.

Where AI Helps, and Where It Does Not

AI earns its place in the parts of the forecast that depend on patterns in history:

• Predicting payment dates. A model trained on paid invoices can predict when each open invoice will be paid from the customer's history, the invoice size, the time of month and the season, and give a range rather than one date.
• Forecasting volume-driven flows. Daily card and cash takings, or receipts from many small customers, follow seasonal patterns that time-series models handle better than a flat average.
• Spotting anomalies. Duplicate supplier bills or unusual payments are worth catching before they reach a payment run.

AI does not know about the contract signed yesterday, the customer who has just disputed an invoice or the shipment that will arrive early. New customers have no history, and one-off events do not repeat. A person still owns the forecast, and every manual adjustment should be logged with its reason, so you can see later whether it helped. The accuracy measures in our guide to AI demand forecasting for retailers and distributors, such as bias and comparison with a simple baseline, apply to cash forecasts too.

Scenarios: Plan for the Weeks That Could Go Wrong

A single forecast hides risk. Keep a base case and add two or three scenarios that each change specific lines:

Who Owns the Forecast

A forecast without an owner decays within weeks. Give it one owner in finance, usually the financial controller or treasurer, and agree who feeds which lines:

• Sales: large expected orders and any customer who has warned of a delay.
• Purchasing: shipment dates, supplier terms and planned large orders.
• HR: joiners, leavers and their end-of-service amounts, and any change to pay dates.
• Finance: tax payments, loan schedules, facility limits and the weekly variance review.
• Management: a short weekly review of the next four weeks and the scenarios, with decisions recorded.

Getting the Data out of Your ERP

The forecast is only as good as the data behind it. Reconcile bank accounts every week, and our guide to bank reconciliation in Odoo shows how rules and bank feeds keep that quick. Record due dates and payment terms on every invoice and bill, post vendor bills when they arrive rather than at month-end, and keep expected dates on purchase orders up to date.

On Odoo Enterprise, the Accounting app's aged receivable and aged payable reports group open invoices and bills by how long they are overdue, and its cash flow statement shows how cash moved in past periods across operating, investing and financing activities. On Odoo Community, the free SBS Account Community module adds aged balances, partner ledgers, budgets and recurring payments.

In StarBiz, receivables, payables, purchasing, payroll and bank reconciliation share one data model, and the AI layer's time-series models, trained on your own history, project the cash position per week with confidence ranges rather than single figures. Managers can also ask questions of live data through StarBot, such as which customers are overdue by more than 60 days.

How Star Bit Solutions Can Help

We build forecasting into the ERP rather than around it. Star Bit Solutions can:

• clean up the data a forecast depends on, from bank reconciliation to payment terms, due dates and purchase order dates;
• set up a rolling 13-week cash flow forecast fed by your ERP, with scenarios and a weekly variance review;
• switch on StarBiz cash forecasting, or connect Odoo to the StarBiz AI layer where it adds value, once there is enough consistent history.

Tell us how you forecast cash today through the contact page, or call +971 55 973 4524, and we will show you what your own ERP data can already tell you.