Worldwide, many studies on Industry 4.0 are being conducted, but the lack of understanding of the essence, scope and complexity of these new technological concepts has caused strategic and operational challenges for some companies. Therefore, in a scenario of a large supply of technological solutions, it is essential that educational institutions and companies can test, experiment and prototype processes for implementing these technologies. The objective of this work is to develop a computer architecture that integrates real and virtual environments in a teaching platform, promoting multidisciplinary skills in engineering with the aim of supporting the implementation, testing and study of Industry 4.0 technologies, preparing education for the new manufacturing scenario. The work uses the method of computer simulation and case study in a Learning Factory. The simulation reproduces the real system virtually, while the case study presents the creation of a computer architecture model based on the ANSI/ISA-95 and VDI-5600 standards, ensuring a standardized environment aligned with the industry, preserving the Learning Factory concept. The results show that it was possible to adapt the automation and install the sensing system and PLC (Programmable Logic Controller) for real-time data collection and transmission. Systems for production management and control, such as MES, APS, MRP and ERP, were installed, as well as CAD and computer simulation software (Tecnomatix, Siemens) for virtualizing the work environment. The software structure made the Learning Factory’s virtual environment possible, integrating with the real environment to teach Industry 4.0 concepts through the integration of technologies. This type of initiative is aligned with global industry development trends that will impact business competitiveness. In the academic context, it allows for small-scale testing and data collection, benefiting students from academia and industry with in-person or remote experiences, with potential for national and international impact in validating hypotheses and testing production processes.