IA preditiva

Define the prediction time and horizon. A forecast for tomorrow, next month, and the next five minutes can require different inputs and evaluation. Check that every input would actually be available when the forecast is issued. Separate prediction from the action taken on it. An inventory forecast estimates demand; a replenishment decision also depends on lead time, storage capacity, shortage costs, and waste. A better numerical score is useful only when it improves the downstream decision. Evaluate against simple baselines and across time periods. Average error can conceal systematic underprediction during peak demand or poor performance on new products. Where appropriate, estimate uncertainty and check how often observations fall inside the reported intervals. Monitor both input changes and measured outcomes after deployment. Feedback may arrive late, and the model’s own decisions can change which outcomes become visible. Record overrides and corrections so a later review can distinguish model errors from missing measurements or policy changes.