Predictive ML / Retail planning
Plan demand without hiding uncertainty.
Historical orders become backtested forecasts, ranges, and planning signals for replenishment, stock, and purchasing teams.
Primary buyer: Retail and e-commerce operatorsInteractive precomputed study
One category. Three planning assumptions.
Switch the assumption to compare a baseline, a promotion, and a constrained-supply case. The series is precomputed and illustrative; a paid pilot uses the client hierarchy and decision horizon.
Seasonality continues without a known intervention and uncertainty expands with the horizon.
What the pilot must prove
- 01
The target matches a real planning decision and usable hierarchy.
- 02
Historical data is representative, timestamped, and free of leakage.
- 03
The approach beats a naive and current operational baseline.
- 04
Uncertainty and failure segments are useful to the planning owner.
Production system
- 01Data ingestion and quality contracts
- 02Backtesting and baseline evaluation
- 03Forecast, range, and scenario generation
- 04Planning workflow and override capture
- 05Monitoring, retraining, and cost controls
Service focus
A forecast you can order stock against
The question is whether the buyer changes the order, and whether the range around the number is honest. We backtest against your own history, compare against the naive baseline you would get for free, and express the output as the decision you actually take.
- Built for
- Retail, supply, manufacturing, and planning leaders
- Designed to achieve
- Better planning against a visible baseline and explicit uncertainty
What you get
- Backtest against your history and a naive baseline
- Prediction intervals, not just a single number
- Output mapped to the replenishment or capacity decision
- Documented limits: where the forecast should not be trusted
FAQ
Questions before a pilot
These answers describe how we normally work. Only a signed agreement creates commitments. Website enquiry terms
01How much history do we need?
Enough to cover the patterns you care about, which usually means at least two full cycles of whatever repeats: seasons, weeks, promotion periods. With less than that we can still build something, but we will be clear that it is a starting point rather than a dependable forecast. Discovery tells you which of the two you have.
02We already forecast in a spreadsheet. Why would this be better?
It might not be, and that is worth finding out cheaply. We backtest against your existing method rather than against nothing, and if the spreadsheet wins on the segments that matter you have saved a production build. More often the gain is not accuracy but coverage: the same quality of judgement applied to ten thousand items instead of the fifty someone has time for.
03What happens when something unprecedented happens?
The model gets it wrong, like everyone else did. What matters is whether the system tells you it is uncertain instead of stating a confident number. We build prediction intervals and the manual override in from the start, because the plan has to survive contact with someone who knows something the data does not.
Need a different system?
Not quite your solution?
Explore nearby systems or bring the outcome, workflow, and constraints that do not fit a predefined category. Tandemora can scope a custom solution around the real operating problem.
Benchmark before replacing the spreadsheet.
A forecasting pilot should establish the baseline, economic error, useful horizon, and exact integration path before production scope is priced.
Design the pilot