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Digitalization in manufacturing is the conversion of information into digital format, the integration of this digital data and technologies into the manufacturing process and the use of those technologies (eg: simulation) to change a business model to provide new revenue and value-producing opportunities. Digitalization may be seen as the increased generation, analysis, and use of data to improve the efficiency of the overall manufacturing system. Simulation in manufacturing is often applied in situations where conducting experiments on a real system is impossible or very difficult due to cost or time to carry out the experiment is too long. A key input to the simulation model of automated equipment is the acquisition of valid data in relation to cycle time and reliability of various workstations on this line. As a consequence of being able to simulate equipment processes and interact with this validated simulation model, both the understanding of how the production system will perform under varying reliability and cycle time conditions is achieved. The simulation model then enables the experimentation of ‘what if scenarios’ that can be tested easily, while also providing a valuable tool to inform the maintenance personnel what station reliabilities they need to target in order to sustain a high performing manufacturing line. Simulation metamodeling is an approach to line design which is of great interest to design engineers and research experts. However, its application in automated medical devices manufacturing line design has never been well explored. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated medical devices manufacturing line in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates with a high level of rigour, fidelity and overall system design/approach, how a digital model along with the use of a metamodel can be used for the development of an automated manufacturing line in the medical devices industry. The digital model and metamodel can be used by manufacturing engineering teams to perform scenario testing during the design and development phase of the line or as part of the continuous improvement stage when the line is in full operation. The overall average absolute error when comparing the simulation model outputs to the metamodel outputs was 0.87% was achieved with the metamodel for the actual industrial application used by the author.