An integrated RAMD-FMECA framework for reliability-centered maintenance and spare parts management: An industrial case study on CNC machining centers

Unplanned downtime in CNC machining centers severely undermines manufacturing competitiveness, particularly in medium-scale enterprises (MSEs) within developing economies where reactive maintenance prevails and spare parts management remains disconnected from operational risk assessment. This study addresses this persistent challenge by proposing and empirically validating an integrated RAMD-FMECA framework that systematically links reliability diagnostics with inventory policy through a novel criticality-based classification matrix. Unlike existing approaches that treat reliability analysis and spare parts management as separate activities, our framework operationalizes the direct translation of Failure Mode, Effects, and Criticality Analysis (FMECA)-derived Risk Priority Numbers (RPN) into inventory stocking decisions, creating a traceable, closed-loop decision support mechanism absent from previous models. Applied through an industrial case study at a metalworking enterprise in Ethiopia, the framework quantified baseline system availability at 85%—significantly below the 95% industry benchmark—and identified spindle assembly and ball screw drives as most critical, with RPN values exceeding 350. The integrated ABC-RPN classification matrix achieved a 75% reduction in stockout days for critical components and 67% reduction in emergency procurement events compared to conventional cost-based classification. Smartphone-based vibration monitoring validated degradation predictions with statistical significance ( R ²  = 0.89, p  < 0.001), estimating remaining useful life of 41–59 days (95% CI). Cost-benefit analysis demonstrated a payback period of 0.25 months and first-year ROI exceeding 4700%, confirmed by Monte Carlo simulation (10,000 iterations; 90% CI for ROI: [1.520%, 5.610%]). The framework provides a practical, replicable blueprint for transitioning from reactive to reliability-centered maintenance in manufacturing settings with limited digitalization and technical capacity, addressing critical gaps in both methodology and practical implementation guidance.