AI-Driven Inventory Optimization in Airline Logistics: Enhancing Efficiency, Sustainability, and Operational Performance

This study examines the application of AI-driven predictive analytics to optimize inventory management in airline logistics. The primary research objectives are to evaluate how these advanced techniques reduce spare-part shortages and excesses, enhance operational efficiency, and contribute indirectly to sustainability by lowering waste and energy consumption. Employing a mixed-methods approach, the research synthesizes quantitative performance metrics such as inventory cost reductions ranging from 25% to 40% and inventory level decreases of 20% to 54% with qualitative insights into strategic implementation and stakeholder engagement. Key findings indicate that integrating machine learning algorithms and simulation-based models not only improves inventory turnover and reduces lead times (by approximately 11.5%) but also achieves near-perfect spare parts availability. The study further highlights substantial financial benefits, including significant cost savings and improved working capital, and outlines actionable recommendations for integrating AI into existing ERP/MRO systems. The results contribute to theoretical frameworks in digital transformation and supply chain management, offering practical implications for airline executives and policymakers seeking to drive operational excellence and sustainable practices in the aviation industry.