Forecast demand that adapts
Traditional forecasts break when market regimes shift. Causify builds demand models that capture structural changes and explain forecast movements to planning teams.
When forecasts fail
Demand planning teams rely on forecasts to allocate inventory, schedule production, and negotiate contracts. When forecasts fail, the business pays in stockouts, excess inventory, or missed revenue.
Traditional time-series models assume stable relationships. When consumer behavior shifts, supply chains reorganize, or macro conditions change, forecasts break. Planners can't explain why or how to fix them.
How Causify helps
Causify forecasts demand by modeling the causal drivers: price elasticity, competitor actions, seasonality, macro indicators, and supply availability. When regimes shift, the causal graph updates and forecasts adapt automatically.
Planners see which factors moved the forecast and can simulate interventions: "If we drop price by 10%, how does demand respond?" Every forecast is explainable to finance and operations.
How it works
Integrate demand data
Connect sales, pricing, inventory, and external signals (weather, macro indicators, competitor data).
Discover demand drivers
Causify identifies which factors causally drive demand and which are downstream effects or noise.
Forecast with causal models
Generate forecasts that adapt to regime changes and capture non-linear relationships.
Explain & simulate
Show planners which drivers moved the forecast and simulate pricing or promotion scenarios.
Forecast demand with causal AI
Talk to our team about demand forecasting for your supply chain.