Who this is for
Owner or operations manager who orders stock from a spreadsheet extrapolation.
Stock has been run out of or overbought, and the next order needs a number better than 'same as last year plus ten percent'.
The result
For one series or up to 50 SKUs, you receive a forecast with an interval and a rolling backtest against naive and seasonal-naive forecasts. If our method does not beat the seasonal-naive baseline by the agreed margin, you receive that finding and the baseline forecast.
What is included
- One series or up to 50 SKUs with at least two full seasonal cycles before the first test period, plus 12 test periods
- A forecast with an 80% interval for the agreed horizon
- A rolling-origin backtest against mean, naive, seasonal-naive and drift methods
- A list of the promotions and holidays used, and stock-out periods flagged
- A plain-English limits note, the script and run steps
You receive
- The forecast file
- The backtest table: error per method and horizon
- The method note and limits
- The list of promotions, holidays and stock-outs used
- The script and run steps
What is not included
- Placing orders or changing stock
- A promise about future accuracy
- Pricing decisions
- Forecasts about individual people
- Series with fewer than two seasonal cycles plus 12 test periods
What we need from you first
- What is forecast (orders, units, revenue) and the horizon
- How many years of history there are, and at what step (daily, weekly, monthly)
- No real records, personal data or credentials in the first enquiry
Never send passwords, keys, customer records or confidential code in the first enquiry. Secure handover is agreed after scoping.
How we check it is done
- Errors are computed only on periods never used for fitting, with at least 12 test periods. The method and its settings are fixed before those periods are scored, and the score is reported once, whatever it shows. Interval coverage is claimed only with at least 30 test periods; otherwise the report says it cannot be judged.
- Our method's scaled error beats the seasonal-naive method by the agreed margin (we propose below 1.0), or the report says 'no improvement' and delivers the baseline forecast.
- Running the script twice gives identical forecasts.
- The promotions, holidays and stock-out periods used are listed, and the report names the series where they weaken the result.
- The report promises no future accuracy. Where a statistical method is what runs, the report says so and does not call it AI forecasting.
You inspect the backtest and the limits, and sign off in writing. Payment follows sign-off, including when the finding is 'no improvement'.
When we would stop or decline
- History is shorter than two seasonal cycles plus 12 test periods
- You want a promise about future accuracy
- You want the system to place orders
- Stock-outs hid most of the demand and the dates are unknown
Questions
Is this AI forecasting?
It is a statistical forecast checked against simple baselines. A model, if used at all, only helps write the summary.
What if your forecast is no better than last year?
Then the report says so, you pay the same, and you get the baseline forecast.
Will it be right?
We cannot promise that. We show how it would have done on periods it never saw.
Price and terms
£295 · untested offer price. £295 after the agreed checks pass and you sign off. A finding of "no improvement" is a valid result and is paid for. No payment before sign-off.
This is a new service with no published client results. The price is a starting point we have not yet tested with buyers. Nothing is ordered or charged by the enquiry. The full specification is on the Synthetic Industry catalogue.