Description: Simulation modeling refers to the creation of a digital representation of a real-world process, allowing for the analysis and prediction of its behavior. This approach is based on the mathematical and computational representation of complex systems, facilitating the understanding of their internal dynamics and the interaction between different variables. In the context of advanced industrial technologies, simulation modeling becomes an essential tool for optimizing processes, improving operational efficiency, and reducing costs. By integrating technologies such as the Internet of Things (IoT) and artificial intelligence, multimodal models allow for the simulation of different scenarios and conditions, providing a holistic view of the system. This not only aids in informed decision-making but also enables virtual testing before implementing changes in the real world, minimizing risks and maximizing outcomes. The ability to model and simulate processes in real-time is crucial for companies looking to quickly adapt to a constantly changing market environment, making simulation modeling a key piece in digital transformation and technological innovation.
History: The concept of simulation modeling has its roots in the 1960s when mathematical models began to be developed to represent complex systems. With the advancement of computing, especially in the 1980s and 1990s, the use of computer simulations became popular in various industries. The advent of advanced industrial technologies in the last decade has further propelled its evolution, integrating advanced technologies such as IoT and big data.
Uses: Simulation modeling is used in various fields, including manufacturing, logistics, healthcare, and urban planning. It allows companies to optimize their processes, foresee potential issues, and conduct ‘what-if’ analyses to assess the impact of different decisions.
Examples: A practical example is the use of simulations in the automotive industry, where production lines are modeled to identify bottlenecks and improve efficiency. Another case is in healthcare, where patient flows are simulated to optimize resource allocation in hospitals.
