A forecast your operation can order against.
Demand forecasting software turns sales history, seasonality and live operational signals into a number teams can order, cook and staff against. CODT Technologies built one in production for the FeelEat group: per-site, per-dish forecasts at 98% accuracy — stockouts down 58%, excess stock down 41%.
From raw signals to tomorrow's plan.
Signals flow in
Two years of sales history, seasonality, calendar effects, weather and live telemetry from the operation move through engineered ingestion pipelines — the platform remembers what a planner's instinct cannot.
Models forecast per site, per dish
Predictions are tuned to the horizon planners actually buy against, at the granularity decisions are made: this dish, at this location. Accuracy is measured continuously in production — 98% against what actually sold.
Forecasts become plans
The output is not a chart that says "it depends" — forecasts translate into order quantities, production plans, staffing and purchasing budgets, all driven by the same demand curve.
Retraining keeps it honest
Menus rotate, sites open, seasons shift. Drift monitoring tracks accuracy against reality, models retrain on fresh data automatically, and new locations start from transfer forecasts that converge as local data arrives.
Accuracy that survived contact with production.
FeelEat demand forecasting — ML over a live operation
For the FeelEat group we built the full loop: signal ingestion, per-site per-dish forecasting models, planning views for inventory, staffing and spend, and the drift monitoring and scheduled retraining that keep 98% honest as demand shifts. The model ran in shadow mode against human plans until its track record — not its math — won the argument, then cut over site by site; the platform paid for itself in the first quarter.
Questions teams ask about demand forecasting
Zet het in een briefing. Een senior engineer — geen verkoper — reageert binnen één werkdag.
Q.01What is demand forecasting software?
A machine-learning platform that turns your sales history, seasonality and live signals into a forward plan — predicting demand at the granularity you decide at, so inventory, staffing and spend are planned against a number instead of a guess.
Q.02How accurate can a demand forecast really be?
The platform we built for the FeelEat group runs at 98% accuracy in production — measured continuously against what actually sold, not a one-off holdout-set result. Accuracy only counts when it is measured where money moves.
Q.03What data does it need to work?
FeelEat's models were trained on two years of sales history plus seasonality, calendar effects, weather and live signals from the operation itself. New sites with no history start from transfer forecasts based on similar locations, which converge as local data arrives.
Q.04What business results follow from better forecasts?
Both directions of error shrink. At FeelEat, stockouts fell 58% and excess stock fell 41% — fewer empty shelves and less fresh food cooked into the bin, from the same demand — and the platform paid for itself in the first quarter.
Q.05Will our planners actually trust a model?
Not on day one — and they shouldn't. FeelEat's model ran in shadow mode next to human plans until its track record won the argument, then cut over site by site. Trust is earned by measurement, and we design the rollout that way.
Q.06Is the model itself the hard part?
No — the model took weeks; the data plumbing, drift monitoring and the planning views someone checks at 8 a.m. took months. That operational layer is what makes 98% accuracy usable, and it is where most of the engineering lives.
Q.07How do we start?
With a paid discovery sprint: we scope your data sources, decision granularity and planning views together, and you receive a fixed, transparent quote before any build begins.
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goed op te lossen?
Vertel ons over uw product, uw planning en uw randvoorwaarden. We reageren binnen één werkdag met een eerlijke inschatting van fit, scope en het juiste team ervoor.