How long will my business cash last?
Last updated 7 August 2026 · written against runway@1.0.0 · by the LEXUN team at CA Capital Limited
The checkable facts: 5,000 simulated paths per run · free, no account or card · your figures stay on this device (the optional Copilot is the one exception, and it sends only what you deliberately type or ask it to summarise) · forecasts frozen before outcomes are known, then Brier-scored in public — misses included.
The formula everyone uses flatters you, because real revenue doesn't arrive in a straight line. Here is the honest way to work out your runway — and a simulation you can reproduce yourself in two minutes.
The straight-line answer (and why it lies)
The formula in every textbook is runway = cash ÷ (monthly costs − monthly revenue). If you have £80,000, spend £18,000 a month and bring in £12,000, you're burning £6,000 a month, so the answer is "about 13 months". Simple.
The problem: that calculation assumes next month's revenue equals this month's, every month, forever. Small-business revenue rarely swings less than 10–15% month to month. Volatility is not noise that averages out — a bad quarter early can end the business even when the averages say you were fine. Straight-line runway is the best case wearing a disguise.
What survival probability actually looks like
The honest question isn't "how many months of average burn do I have?" but "what fraction of the plausible futures keep my cash above zero for the horizon I care about?" That needs a simulation: thousands of possible revenue paths with your stated growth and volatility, counting how many survive.
A real worked example (run on this site's engine — deterministic, so you'll get the identical result):
cash £80,000 · revenue £12,000/mo · costs £18,000/mo · growth 2%/mo · volatility 15%/mo · horizon 12m
Straight-line says ~13 months. The simulation says the survival probability over 12 months is 60–70% — roughly one in three plausible futures runs out of cash — and the plausible runway range is 9 to 12+ months (12+ meaning the path was still solvent when the 12-month window closed — month 13 was never simulated). Same numbers, very different decision.
The three numbers that move the answer most
- Monthly costs. In the example above, ±20% on costs swings survival by more than any other input. Costs are also the number you control most directly — which is why cost discipline beats revenue optimism in a cash crisis.
- Revenue volatility. Two businesses with identical averages and different volatility have very different survival odds. If one customer is more than ~20% of revenue, your true volatility is lumpier than your spreadsheet shows.
- Growth assumptions. Compound monthly growth above ~5% is very hard to sustain for a year. If your survival verdict flips when you halve your growth assumption, the decision is resting on hope.
The environment you're operating in
For context, not destiny: 38.4% of UK businesses born in 2019 were still trading five years later (ONS Business Demography 2024). Roughly three in five don't make it to year five — which is exactly why watching your cash honestly matters more than feeling optimistic about it.
What no calculator can tell you: whether a specific customer will churn, whether that invoice will pay on time, or whether you'll land the contract. A good runway model doesn't predict these — it shows you how exposed you are if they go wrong, and refuses to answer at all when the inputs are guesses. LEXUN's engine does exactly that: it declines to run on unknowns rather than inventing values, and it shows every result as an honest range, never a false point.
Work out yours
The LEXUN runway analysis runs entirely in your browser — your figures stay on this device; the optional Copilot is the one exception, and it sends only what you deliberately type or ask it to summarise. Enter your cash, revenue, costs and volatility, mark which are facts and which are assumptions, and get a survival band, the drivers that move it, a stress scenario, and a frozen criterion so you can come back and check whether we were right.
Run the runway analysis →Method note — maths, assumptions, failure modes
The maths. Cash evolves monthly: revenue follows a log-normal multiplicative step, revt = revt−1 × exp(ln(1 + g) − σ²/2 + σ·ε) with ε ~ N(0,1); costs grow at a stated rate, committed one-off flows land in their month, and a path ends when cash ≤ 0. The −σ²/2 term makes the expected monthly multiplier exactly 1 + g at every volatility, so raising volatility widens the spread of outcomes rather than adding growth to them, and the step is strictly positive, so no single month can set revenue to zero. (It could before: the earlier form was × (1 + g + σ·ε) floored at zero, and at the 80%-a-month volatility this site allows, 64% of paths had revenue destroyed by arithmetic rather than by trading — see the changelog.) 5,000 seeded paths (mulberry32 PRNG, Box–Muller normals) run in antithetic pairs — each block of monthly shocks used once as drawn and once negated, which cuts the Monte-Carlo error by about a third for the same work — give P(survival) with a binomial standard error that is displayed, never hidden. Bands are the honest output, not a stylistic choice.
Key assumptions. Monthly shocks are independent and normal (real revenue is often autocorrelated and fat-tailed — the stress scenario partly compensates); costs are deterministic apart from the stated growth; no funding events are modelled — a raise, loan or large invoice must be entered as a committed inflow.
Failure modes. Pre-revenue firms with lumpy invoicing violate the smooth-revenue assumption; volatility under ~10%/mo is rarely true for small firms and will flatter the result; growth beyond ±50%/mo or volatility beyond 80%/mo is refused as outside model validity. When most paths survive the horizon, runway percentiles read "12+" — the model does not extrapolate beyond the horizon you set.
References. Survival framing: ONS Business Demography 2024 (five-year survival 38.4%, environment context only). Scoring: Brier (1950); calibration methodology on the Accuracy Centre. Unfamiliar terms — survival band, frozen criterion, Brier score, calibration — are defined in the decision glossary.
Related guides. The same method applied to two other decisions: leaving a job, and a large purchase or commitment. To satisfy yourself the numbers are real before you rely on any of them, re-run a published result yourself and check it against the calibration record.
Stated weaknesses — what this model cannot see
Every model is a simplification, and a model that will not name its own is asking for trust it has not earned. These are runway@1.0.0's, stated from the recurrence itself, not from marketing:
One revenue stream, one volatility. A single monthly shock scales your whole revenue. Losing one large client — a discrete cliff, not a wobble — is exactly the event this shape cannot represent, and for many small firms it is the event that matters.
Months don't remember each other. Shocks are independent month to month. Real downturns cluster: a bad quarter makes the next month worse, and this model does not know that.
Costs never shock. Costs compound smoothly at the growth rate you give. A lawsuit, a rent review, an emergency hire — the model has nowhere for them to land.
Committed amounts land in full, on the booked month. A funding round that slips six weeks does not slip here.
Insolvency is cash at or below £0, full stop. No overdraft headroom, no invoice factoring, no emergency bridge. Real businesses have messier endings, in both directions.
No seasonality, no VAT or tax timing.
Sensitivity probes shift each driver ±20%. Near a cliff edge every driver saturates at ±100 pts — a true statement about your situation, but not a ranking anymore.
If your decision hinges on one of these blind spots, this model is the wrong tool, and the honest move is to say so — which is what the insufficient-evidence state exists for.