Feature · 📈
Demand Forecast
Tomorrow's revenue with a confidence range, modelled on your POS history plus the Indian calendar, weather, and salary cycles.
What it does
Demand Forecast in plain English
The forecast engine takes your POS sales history and projects tomorrow's revenue with a confidence range. It uses the Indian calendar (Shravan, Navratri, Ekadashi, Eid, Diwali, IPL) as exogenous signals, weather as another, and salary cycles as a third. The model is statistical (statsforecast AutoARIMA with cross-features), not a black box, so we can show you exactly which signal moved tomorrow's number.
Examples
What you actually see
₹16,800 forecast tomorrow (-44% vs average — Shravan starts in 4 days, Monday fasting, college exam week).
₹35,200 forecast (-12% — heavy rain expected through dinner; aggregator orders should partially compensate).
₹62,400 forecast (+38% — IPL Mumbai vs Chennai at 7:30pm, salary day Friday).
Inputs
What powers it
- POS sales history (Petpooja, UrbanPiper, Posist, or CSV)
- Indian calendar — festivals, fasting days, Hindu wedding muhurat, Eid, Christmas, school-board exam windows
- Weather (OpenWeatherMap forecast for your pincode)
- IPL match schedule
- Salary cycle (1st-of-month, last-week-pre-salary)
FAQ
Demand Forecast FAQ
How accurate is the forecast?
On our pilot outlets, 14-day rolling MAPE (mean absolute percent error) sits in the 8–14% band — better than ±2,000 INR for a typical Indian QSR doing ₹20K-25K/day. Accuracy improves as we ingest more of your history.
What's the minimum data I need to onboard?
30 days of POS history is the practical minimum; 90 days is the sweet spot. With less than 30 days we use cuisine + format + city defaults until your own data dominates.
Does it handle festivals correctly for non-veg restaurants?
Yes. The model knows that Shravan / Navratri / Ekadashi swing non-veg demand sharply down (and pure-veg / paneer-heavy items up). It models this distinction per dish, not per outlet.
Related reading
Get started
See demand forecast on your own data
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