Demand forecasting is a critical input for resilient production planning, inventory replenishment, procurement, and capacity decisions under demand intermittency, high variability, and operational uncertainty. In these contexts, selecting forecasting models solely on the basis of fixed test-horizon performance may lead to decisions misaligned with the future planning horizons in which forecasts are used. This study proposes the Metric Degradation by Forecast Horizon (MDFH) procedure as a horizon-aware decision-support framework for selecting demand forecasting models. MDFH projects eligible out-of-sample error metrics, specifically MAE, RMSE, and RMSSE, from an observed test horizon toward future operational horizons under explicit structural-stability conditions. Based on this layer, RMSSEh is derived as a parsimonious horizon-aware selector, while the Adaptive Hybrid Selector for Intermittency and Variability (AHSIV) is proposed as an adaptive extension for structurally heterogeneous demand series. ERA, a multivariate ranking-aggregation selector, is included as a comparator. The empirical evaluation uses the Walmart, M3, M4, and M5 datasets, three training-testing partitions, 22 forecasting models, and 12-step future horizons. Results show that RMSSEh and AHSIV provide more consistent downstream volumetric alignment than ERA when assessed through ex post Global Relative Accuracy.