Demand forecasting
When ARIMA, Prophet, regression, and hierarchical forecasts help—and why the hard part is usually the operating system around the model.
Start with the decision
A forecast for purchasing has different costs than a forecast for staffing or cash planning. The horizon, level of detail, update cadence, and penalty for over- versus under-forecasting should be chosen around that decision.
ARIMA and classical time series
ARIMA-family models are strong baselines when a series has stable autocorrelation and enough history. They are interpretable, fast, and often difficult to beat for mature products with consistent patterns.
Prophet and business seasonality
Prophet can be useful when holiday effects, multiple seasonal cycles, and missing observations need to be modeled quickly. It is a practical tool—not a universal winner—and should be compared with simpler baselines.
What production forecasting adds
Retail and CPG forecasts often need promotions, price, weather, distribution, product hierarchy, stockouts, and launch or discontinuation logic. Backtesting, reconciliation, exception handling, and human overrides matter as much as the model name.