Coverage Adequacy as an Alternative to Demand Forecasting for Aftermarket Spare Parts: The DNEIF Framework

Coverage Adequacy as an Alternative to Demand Forecasting for Aftermarket Spare Parts: The DNEIF Framework | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 April 2026 V1 Latest version Share on Coverage Adequacy as an Alternative to Demand Forecasting for Aftermarket Spare Parts: The DNEIF Framework Author : Arjun Pardasani 0009-0000-0961-2036 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177516559.93068302/v1 131 views 52 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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: (1) normalize 36 months of consumption history via IQR filtering, (2) project a 10-year demand ceiling using category-specific geometric decay, and (3) 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. Supplementary Material File (coverage adequacy as an alternative to demand forecasting for aftermarket spare parts- the dneif framework.pdf) Download 660.16 KB Information & Authors Information Version history V1 Version 1 02 April 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords aftermarket inventory coverage adequacy demand forecast demand normalization durable goods manufacturing excess stock classification intermittent demand spare parts optimization supply chain supply chains working capital recovery Authors Affiliations Arjun Pardasani 0009-0000-0961-2036 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 131 views 52 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Arjun Pardasani. Coverage Adequacy as an Alternative to Demand Forecasting for Aftermarket Spare Parts: The DNEIF Framework. Authorea . 02 April 2026. 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