11/08/2026
Here’s an uncomfortable truth about most cash flow forecasts: by the time someone finishes building this month’s version, it’s already a little out of date.
Forecast accuracy is typically measured as: one minus the absolute difference between actual and forecast, divided by actual, times 100. Almost no company
hits the number exactly – and that’s fine. Most finance teams set a deliberate acceptable variance target, commonly around 5%, rather than expecting a
perfect hit.
The real problem isn’t the miss. It’s why forecasts drift more than they need to: most rely on historical patterns and periodic, manually-assembled inputs – AR,
AP, and bank data living in separate systems, pulled together whenever someone gets around to it. When a few large customers quietly extend their payment
terms, the forecast has no way of knowing until the gap shows up in actuals.
EY documents a real example of what fixing this looks like: one company connected operational stakeholders and process knowledge directly to its cash
forecast, and reduced its forecasting variance by USD 450-535 million. That’s one company’s real, documented result – not a claim every business would see
the same outcome.
The fix isn’t a better spreadsheet template. It’s continuous, multi-source input feeding the forecast as data changes, instead of a periodic manual roll-up built
from whatever was true last time someone updated it.
Does your team know your current forecast-to-actual variance right now, or only at month-end?
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