The advent of additive manufacturing, also known as 3D printing, within the framework of Industry 4.0 has significantly transformed traditional production processes by enabling highly customizable and efficient manufacturing solutions. Unlike conventional methods, additive manufacturing supports decentralized production, which reduces the need for large-scale, centralized factories and enhances the flexibility and adaptability of the manufacturing sector. This decentralization allows manufacturers to produce closer to demand points, respond swiftly to customer requirements, and minimize inventory costs. This paper focuses on evaluating the performance of 3D Additive Manufacturing Printers by analyzing the impact of random equipment failures, repair times, and scrap and rework probabilities of non-conforming parts on the Overall Equipment Effectiveness of these machines. This is accomplished by developing analytical models that assess the effectiveness of 3D printing machines under different industrial scenarios, guided by the specific policies of production and quality management departments. Additionally, simulation models are constructed to mimic the real-world dynamic and stochastic behavior of these Additive Manufacturing Printers, providing a robust validation of the proposed analytical approaches. According to specific industrial contexts, and based on several configurations of Additive Manufacturing Printers, the results show that the proposed analytical models generate a negligible relative error, inferior to 1%, compared to simulation ones, providing insights for improving the efficiency and reliability of additive manufacturing processes across various industrial contexts.