<p>Spare parts inventory management in asset-intensive industries such as petrochemicals, power generation, and manufacturing is increasingly complex due to large SKU portfolios, high capital lock-in, and demand uncertainty driven by equipment criticality and supply-chain disruptions. Conventional classification-based approaches (e.g., ABC, FSN) inadequately capture equipment failure behavior, service criticality, and lead-time variability, often resulting in excess inventory and costly stockouts. This study proposes the Spare Parts Inventory Control of Equipment (SPICE) framework, a quantitative, system-oriented model that integrates equipment reliability, service criticality, procurement lead times, and economic constraints into analytically derived inventory decision boundaries. The model establishes three coordinated stock thresholds to balance availability, resilience, and capital efficiency under stochastic operating conditions. The framework is validated through a industry expert surveys, and pilot implementations in capital-intensive industrial settings. Results indicate that reliability- and criticality-driven stocking significantly reduces emergency outages, buffers against lead-time uncertainty, and lowers excess inventory without compromising service levels. Pilot applications demonstrate inventory cost reductions of 15–20% while maintaining service availability above 99.8% for critical equipment. By unifying failure mode analysis, classical inventory theory, and cost-based controls into a single analytical structure, SPICE bridges the gap between inventory theory and industrial practice. The framework offers a scalable and actionable decision-support tool for optimizing spare parts inventory in complex, high-value industrial environments.</p>