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CONCEPT Cited by 1 source

Probabilistic demand forecast

Definition

A probabilistic demand forecast is a forecast whose output is not a single point estimate but a distribution over possible future demand values — typically parameterised by quantiles, prediction intervals, or a full density function. Required upstream input for any optimisation under uncertainty formulation of inventory / replenishment / capacity problems, because the optimiser needs to reason about the shape of the risk, not just the mean.

Why it matters for replenishment

Point forecasts tell you "the expected demand next week is 47 units"; they don't tell you whether the realistic range is [40, 55] (tight; small safety stock suffices) or [0, 200] (wide; order more to avoid stockouts, accept some overstock risk). A stockout vs overstock decision is fundamentally asymmetric — a probabilistic forecast exposes that asymmetry to the optimiser, which can then explicitly weigh C_lost_sales against C_overstock via Monte Carlo simulation.

Producing probabilistic forecasts

Mechanisms observed on the wiki:

  • Conformal inference wrappers around point-forecast models (e.g. Nixtla MLForecast around LightGBM point forecasts, producing calibrated prediction intervals).
  • Native probabilistic models (quantile regression, TFT, DeepAR) — mentioned as Zalando alternatives that lost to LightGBM + MLForecast on prototyping speed + ecosystem.
  • Parametric assumption + empirical residuals.

Canonical instance (Zalando ZEOS, 2025-06-29)

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