Aftermarket spare parts inventories in durable goods manufacturing are systematically oversized because planners overstock defensively against uncertain, intermittent demand. Existing remedies either forecast demand at the SKU level, where accuracy is structurally poor, or segment the catalog into policy buckets (ABC-XYZ) without quantifying unit-level excess. This paper proposes a different framing. The Demand-Normalized Excess Identification Framework (DNEIF) treats aftermarket inventory optimization as a coverage adequacy test: rather than predicting what demand will be, it determines whether on-hand stock exceeds what any plausible demand trajectory could consume over a defined service horizon. The framework proceeds in three stages: normalize 36 months of consumption history via IQR filtering, project a 10-year demand ceiling using category-specific geometric decay, and classify each on-hand unit as right-sized, excess, or zero-movement. A simulation calibrated to published industry parameters (11,200 SKUs, $14.8M in inventory, 3.1% utilization) identifies $4.2M (28%) as excess and $820K as zero-movement stock. Six product families concentrate 64% of excess value. Sensitivity analysis confirms classification stability under ±10% perturbation of decay factors. DNEIF requires only standard ERP transactional data and produces transparent, dollar-valued, SKU-level output that neither forecasting models nor segmentation schemes provide.

Keywords

Aftermarket inventory, coverage adequacy, excess stock classification, intermittent demand, and spare parts optimization.