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Stock Control in Airline Logistics: AI-Driven Inventory Optimization for Spare Parts
This paper examines the transformative role of artificial intelligence in optimizing inventory management for airline logistics, specifically focusing on spare parts. The study aims to assess how AI-driven techniques improve operational efficiency, reduce costs, and enhance service reliability through advanced forecasting, automated replenishment, and real-time data analytics. A systematic literature review was conducted, incorporating empirical data, case studies, and mathematical modeling approaches to analyze the performance of methods such as Monte Carlo simulation, Bayesian forecasting, and neural networks. The results indicate significant improvements in inventory control, with cost reductions ranging from 2.4% to 60%, stock level decreases between 34% and 54%, and enhanced fill rates reaching up to 80.58%. These findings highlight the potential of AI to drive predictive maintenance, facilitate real-time inventory tracking, and improve supplier coordination, ultimately contributing to more strategic decision-making in airline logistics. The research provides a comprehensive framework for integrating AI-based solutions into existing systems, underscoring both the practical benefits and the challenges associated with transitioning from legacy infrastructures to modern, data-driven operations.
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