A hybrid AHP-fuzzy inference framework for marine spare parts optimization integrating reliability data

Maritime spare part management often struggles to align reliability data with operational realities. Traditional models neglect supply chain constraints and standard MCDM methods rely on subjective weights. This paper introduces a novel Hybrid Reliability Framework integrating the Analytic Hierarchy Process (AHP) and a Mamdani Fuzzy Inference System (FIS). AHP mathematically validates Operational Criticality and Supply Difficulty as primary drivers with 48.8% decision weight. Using a 7-year dataset (2018–2025) from 10 offshore vessels, the model evaluates 15 critical engine-room systems. Crucially, it resolves the ‘Insurance Spare’ paradox by prioritising high-risk/low-frequency assets such as Emergency Shutdown Systems and components with high supply latency such as Reduction Gears often missed by conventional models. The framework outputs a quantifiable Stocking Priority Index (SPI), equipping maintenance managers to transition from theoritical reliability estimations to actionable, safety-driven inventory optimisation.