Bayesian structural time series

Advanced forecasting should be judged by horizon, segment and decision value—not one aggregate score. This note examines bayesian structural time series with that evaluation discipline.

Practitioner lens

Forecast distributions from bayesian structural time series become actionable when passed into inventory, sourcing or capacity models. Preserving uncertainty is often more useful than collapsing it too early.

First experiment

Compare with naive and current-production baselines, then test sensitivity across product and lifecycle cohorts.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.