Your cash
Money coming in
Money going out
Fill in your cash and lines, then click Run the forecast.
Forecast
Balance by week
| Week | From | Bad case (10th pct) | Middle | Good case (90th pct) | Chance short by now |
|---|
What to fix first
| Change | Chance of shortfall after | 9-in-10 cushion after |
|---|
What it does, and what it leaves to you
- Counts: each invoice is paid on its due date with the on-time probability you set; otherwise it arrives later by a random delay averaging the days you set (exponential, at least one day), or never with the never-paid probability. Bills and repeating lines are fixed on their dates. The simulation uses a fixed seed, so the same inputs always give the same answer. Overdue invoices dated before the start are treated as due on day one. The levers replay the same random outcomes with one line changed.
- Not decided: whether your on-time share and delays are right, seasonality and new sales, tax timing, interest, whether a customer or supplier would agree to a different date, and any advice about financing. Customers who pay late are often correlated; a bad month for one is often a bad month for several, which this simple model understates.
Common questions
What is a cash-flow forecast?
A week-by-week list of the money you expect to receive and the money you must pay, starting from the cash you have today. Its job is to show the week your balance gets thin before it does.
Why simulate late payments instead of assuming invoices arrive on time?
A forecast that treats every invoice as paid on its due date shows one tidy line and hides the risk. Real customers pay late or not at all, so this tool draws a delay for each invoice, thousands of times, and reports how often your balance falls below the floor you set.
What does 'cushion to cover 9 in 10 outcomes' mean?
In 90% of the simulated outcomes the lowest balance never fell short of your floor by more than that amount. It is the cash or credit line that would have kept you above the floor in nine out of ten runs of the same assumptions.
How are the 'biggest levers' worked out?
Each large line is changed on its own and the same simulated outcomes are replayed: an invoice is collected on its due date, or a bill is paid 14 days later. The tool then compares the chance of a shortfall and the cushion needed. It shows what the arithmetic says, not whether a customer or supplier would agree.
Where do the on-time and days-late figures come from?
From you. Use your own history: of the last twenty invoices, how many were paid by the due date, and how late were the rest. The defaults are placeholders, not an industry statistic.
Is my data uploaded anywhere?
No. The lines you paste stay in this page, the simulation runs in your browser, and a test in the code asserts there are no network calls.