Digital twins (DT) are one of the key pillars of industrial transformation toward the Industry 4.0 concept. By integrating process modeling, data analytics, and real-time communication, DTs enable accurate virtual representations of physical systems. Recently, their application has expanded beyond industrial practice to research and education, particularly within smart factory and learning factory demonstrators. These laboratories bridge theory and practice, supporting the development of engineering competencies and experimentation with cyber-physical systems, simulation, and control. In academic environments, digital twins play a dual role as both educational tools and research platforms. They enable controlled, repeatable experiments and systematic evaluation of process parameters, supporting research in automation, robotics, and data-driven decision-making. When connected to real-time data from sensors and PLC controllers, DT-based models achieve a high level of realism, facilitating studies on system interoperability, lifecycle management, and real-time operational optimization. This article provides a concise overview of the role of digital twins in modern demonstrator laboratories, highlighting their importance for research, innovation, and engineering education. In addition, it presents a conceptual application of a digital twin within a smart factory model, serving as an integrated experimental and educational platform for testing advanced manufacturing and control solutions.