Demand Forecasting Explainer
Demand forecasting estimates how much customers may want in a future period. Businesses use forecasts to plan inventory, capacity, staffing, cash, purchasing, and distribution. A forecast is an informed estimate with uncertainty, not a commitment from the market.
Methods range from expert judgment and customer research to time-series and causal models. The right method depends on data, product maturity, horizon, granularity, and the decision. New products may rely more on analogs and experiments, while stable items may support statistical patterns.
Seasonality, promotions, price changes, stockouts, channel inventory, product substitution, and exceptional events can distort history. Forecasts should be reconciled across products, locations, and time horizons. Teams also need to separate unconstrained demand from sales limited by unavailable supply.
Measure error and bias, compare against a simple baseline, and publish ranges or scenarios where uncertainty matters. A sophisticated model that no one can challenge may be less useful than a transparent one. Update plans as evidence arrives without rewriting the original forecast record.
Horizon, product maturity, data quality, and planning use should shape the method.
No single model is best for every level and period.
Stockouts, promotions, substitutions, and one-off events can misrepresent underlying demand.
Document adjustments and preserve raw data.
Compare forecasts with actual demand and a simple baseline.
Persistent over- or under-forecasting deserves investigation.
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