Computer simulation models are widely used to replicate and analyze stochastic systems, with Discrete Event Simulation (DES) being the most common technique to represent industrial settings. However, as the need to adapt to dynamic scenarios grows, Digital Twin (DT) technology has gained significant importance amid the Industry 4.0 landscape as an efficient alternative for designing and evaluating manufacturing systems. As DTs become a widespread technology, it raises the question of how to utilize and improve traditional DES models to incorporate DT capabilities and facilitate decision-making in a constantly evolving environment. This research presents a methodology to leverage a DES model to build a DT by enhancing its capabilities incrementally. The methodology provides a detailed guide for developers to create DTs that can overcome the limitations of traditional DES models, particularly in supporting dynamic, real-time scenarios. The main advantage of upgrading to DTs is their ability to operate and update in real-time (online) as opposed to the static (offline) nature of DES models, thus enhancing decision-making in constantly changing environments. Furthermore, the methodology is validated using a case study at an automotive engine manufacturing plant to transform a fully capable DES into a DT capable of replicating a pallet elevator.