Our study has addressed the tension between operational reliability and environmental responsibility in the aviation spare parts management field . The framework integrates three components: sustainability-weighted hybrid forecasting, multi-objective inventory optimization and a circular economy recommendation engine informed by historical data of maintenance. Validation on a synthetic, industry-calibrated dataset yields an 18.7\% reduction in total annual costs and a 23.4\% decrease in carbon footprint, with service levels held above 93\%. These figures suggest that predictive analytics and sustainable logistics are not competing priorities in the aviation MRO context---they can reinforce each other.

Our research has tackled the conflict between maintaining operational reliability and upholding environmental responsibility in aviation spare parts management. The proposed framework combines three key elements: hybrid forecasting that accounts for sustainability factors, inventory optimization targeting multiple objectives, and a circular economy recommendation system that draws from maintenance history data. Testing with a synthetic dataset calibrated to industry standards demonstrated an 18.7% reduction in total yearly costs and a 23.4% drop in carbon emissions, while maintaining service levels above 93%. These results indicate that predictive analytics and sustainable logistics do not represent conflicting goals in aviation MRO operations - instead, they can support and strengthen one another.