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 FeelEat: forecasts per site and per dish at 98% accuracy, with stockouts down 58% and excess stock down 41%.
How the forecast is built.
Signals flow in
Sales history, seasonality, calendar effects, weather and live telemetry from the operation flow through ingestion pipelines. The platform keeps track of far more history than any planner can hold in their head.
Models forecast per site, per dish
Forecasts match the horizon planners actually buy for, at the level where decisions are made: this dish, at this location. Accuracy is measured all the time in production, and it sits at 98% against actual sales.
Forecasts become plans
Forecasts turn into order quantities, production plans, staffing and purchasing budgets, all driven by the same demand curve.
Retraining as demand changes
Menus change, new sites open and seasons turn. Drift monitoring compares accuracy with reality, and models retrain on fresh data automatically.
98% accuracy, measured in production.
FeelEat demand forecasting — ML over a live operation
For FeelEat we built the full loop: signal ingestion, forecasting models per site and per dish, planning views, and the drift monitoring and scheduled retraining that hold accuracy at 98%.
Questions teams ask about demand forecasting
Opisz to w briefie. Odpowie doświadczony inżynier — nie handlowiec — w ciągu jednego dnia roboczego.
What is demand forecasting software?
A machine-learning platform that turns your sales history, seasonality and live signals into a forward plan. It predicts demand at the level where you make decisions, down to one dish at one site.
How accurate can a demand forecast really be?
The platform we built for FeelEat runs at 98% accuracy in production. We measure it continuously against what actually sold, which tells you more than a single test on held-back data.
What data does it need to work?
FeelEat's models were trained on its 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.
What business results follow from better forecasts?
Errors in both directions shrink. At FeelEat, stockouts fell 58% and excess stock fell 41%, which means fewer empty shelves and less fresh food thrown away.
Will our planners actually trust a model?
Not on day one, and that is fine. FeelEat’s model ran in shadow mode next to human plans until its results held up, then went live one site at a time. We plan your rollout the same way.
Is the model itself the hard part?
No. The model took weeks. The data plumbing, the drift monitoring and the planning views someone opens at 8 a.m. took months. That operational layer is what makes 98% accuracy usable, and it is where most of the engineering goes.
How do we start?
Through a paid discovery sprint that covers your data sources, the level of detail your decisions need and the planning views your team will use. It ends with a fixed quote, and only then does the build begin.
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