Making a decision two weeks early is worth more than making the right decision too late
Forecasts are not about guessing. They are about gaining reaction time.
A warehouse that knows in advance which product will sell can order more effectively. A sales team that knows which customers are about to leave can call them before they do. A finance team that sees a cash flow squeeze coming can manage it instead of reacting to it.
The value of a forecast does not lie in absolute accuracy, but in the time it gives you to react before the decision would have to be made anyway.
The questions we answer most often
- Demand and inventory: how much of this product will I sell in the coming weeks? This helps you order the right amount: fewer stockouts, less capital tied up in inventory.
- Sales: how much of the pipeline will actually close this quarter? This helps you plan capacity and budget on a solid basis.
- Cash flow: when will I realistically collect payment? The contractual due date is a poor predictor of the actual collection date; historical payment behavior is much more reliable.
- Customers: which customers are about to leave us? This gives you time to intervene while there is still an opportunity to retain them.
- Maintenance: which machine is likely to fail? This allows you to intervene before the downtime, not after it.
One clarification we always make before starting
A forecast is a range, not a single number. “We will sell between 180 and 220 units” is honest and usable information; “we will sell 200 units” is the same information presented in a misleading way.
We always provide the range because it is what allows you to decide how much risk to take. And if the historical data is insufficient or its quality is not reliable enough, we say so before starting the project rather than delivering a model that cannot hold up.
What it takes to get started
Enough historical data to cover at least two complete seasonal cycles, a level of granularity consistent with the decision being made — forecast by week if you make weekly decisions — and known external factors that influenced the past: promotions, closures, price changes, exceptional events.
The last point is the one most often overlooked. Without information about past promotions, the model attributes what was actually a marketing campaign to seasonality and systematically gets it wrong.
How we verify that the model actually works
Not on the data it was built on. Validation is performed by simulating the past: we go back to an earlier point in time, generate a forecast using only what was known at that moment, and compare it with what actually happened. Repeated across multiple points in time, this produces a credible measure of accuracy.
Each model is then compared with the simplest possible forecast — the value from the same period the previous year, or the recent average. If it does not clearly outperform it, it does not go into production: a complex model that performs no better than a simple rule is just an additional maintenance cost.
What we deliver
A forecast for each period with its prediction range, measured historical accuracy, an indication of the factors that have the greatest impact, and continuous error monitoring. When the error moves outside the expected limits, an alert is triggered: this means something in the real world has changed and the model needs to be reviewed, not that something has simply broken.