Manufacturing has undergone significant transformations throughout the Industrial Revolutions, evolving from manual processes to advanced automation using machines. Industry 4.0 is characterized by technologies such as the Internet of Things (IoT), Cloud Computing, Big Data, Robotics, and Artificial Intelligence (AI), which aim to develop intelligent manufacturing through Cyber-Physical Production Systems. In this context, production lines need to be adaptable, smart, and flexible. In this context, Digital Twins (DT) play a crucial role by enabling simulations and optimizations before real-world implementation, improving efficiency, and reducing errors. Digital Twins are virtual representations of physical assets, processes, or systems that enable simulations and tests to optimize operations, addressing challenges such as constantly changing customer expectations and harsh environmental conditions. They allow data-driven decision-making, predictive maintenance, and increased operational efficiency, reducing the risk of errors, and extending the product lifecycle. The growing complexity of manufacturing systems demands innovative approaches like Model-Based Systems Engineering (MBSE), which, when integrated with Digital Twins, offers a comprehensive approach for the development, operation, and optimization of systems. Continuous data synchronization between the digital twin and its physical counterpart ensures accurate representation, enhancing decision-making and performance management throughout the system’s lifecycle. The present work aims to answer how MBSE can be used in the creation of Digital Twins for manufacturing systems and what are the benefits and disadvantages of this adoption. The objective is to demonstrate, through a framework and a case study in a Learning Factory, how executable MBSE diagrams can generate effective Digital Twins.