An Adaptive Model Selection Framework for Demand Forecasting under Horizon-Induced Degradation to Support Business Strategy and Operations

arXiv:2602.13939v3 Announce Type: replace Abstract: Business environments characterized by intermittent demand, high variability, and multi-step planning horizons require forecasting policies that support consistent operational decisions across heterogeneous SKU portfolios. Because no forecasting model is universally dominant, and model rankings vary across error metrics, demand regimes, and forecast horizons, forecasting model assignment is a nontrivial decision problem in inventory planning, procurement, and supply management. This study proposes the Adaptive Hybrid Selector for Intermittency and Variability (AHSIV), a horizon-aware and regime-conditioned model selection framework designed to address horizon-induced ranking instability. The framework integrates the Metric Degradation by Forecast Horizon (MDFH), structural demand classification, multi-objective Pareto dominance, and hierarchical bias refinement within a unified decision architecture. The empirical evaluation is conducted on the Walmart, M3, M4, and M5 datasets under multiple train-test partition schemes and twelve-step forecasting horizons. The results show that AHSIV achieves statistical equivalence with the strongest single-metric baseline in aggregated performance while improving the consistency of model assignment across forecast horizons in heterogeneous demand settings. These findings indicate that forecasting model selection in multi-SKU environments should not be treated as a static ranking problem. Instead, horizon-consistent and structurally adaptive selection mechanisms can provide more robust support for inventory planning, procurement alignment, and operational decision-making in business forecasting systems.