# Optimization Processes in Automotive Logistic Flow
## 4 Conclusions  
This paper leverages the advanced capabilities of the WITNESS Horizon simulation software to craft a virtual model of an automotive logistic flow. This digital twin of manufacturing architecture is a versatile tool that can assess various manufacturing scenarios and predict the evolution of performance parameters under changing conditions, such as worker fatigue, demand fluctuations, or equipment failures. The virtual model was calibrated, comparing simulation output with actual production data to ensure the model accurately reflects real-world operations. In order to obtain improvements in production capacity and operational efficiency, the model was used to diagnose the performance indicator levels and identify the necessary changes. The focus was on identifying and validating the best parameters and the configuration of the manufacturing architecture. After using the virtual prototype to simulate the initial logistic flow, reports were generated, and the results obtained for each workstation were interpreted to identify the flow concentrators. The necessary changes to workstations were described, and the system architecture was modified accordingly. After these changes were implemented, detailed simulation results and optimization outputs (e.g., a 52% increase in production capacity, from 2618 to 3980 rear axles per week) were highlighted, demonstrating the significant impact of the optimization. The Simplex method proved effective in solving the optimization problem of producing rear non-motorized axles, ensuring the identification of an optimal solution in a relatively small number of iterations. This demonstrates the utility and applicability of linear programming methods in production planning and optimization. The case study illustrated a complex process of optimizing production capacity and operational efficiency in an industrial environment. In conclusion, the case study demonstrates the success of a strategic and well-managed approach to optimizing industrial processes. Implementing changes and appropriate technologies leads to significant improvements in production capacity, operational efficiency, and logistics management, strengthening a company's position in the face of market challenges and ensuring a solid foundation for future growth. To improve operational performance, the model could be used to explore options for diversifying the product range, optimizing the supply chain, and evaluating the impact of new technologies to increase production efficiency. This research, by combining the virtual model functions with innovative strategies and advanced technologies, illustrates how significant results could be achieved in several key areas:  
1. Improved production capacity: changes in the production architecture significantly increased production capacity from 2618 to 3980 rear axles per week, demonstrating effective optimization of existing processes. 2. Improved operational efficiency: the reduced cycle times at various workstations improved resource use and significantly enhanced overall operational efficiency in the production line. 3. Logistics optimization: a crucial part of the optimization process involved replacing forklifts with conveyors. This change eliminated logistics bottlenecks and enhanced the flow of materials, leading to improved synchronization of operations and overall logistics optimization. 4. Effective use of advanced technologies: the integration and improvement in robotic welding cells, which offer high-speed and high-precision welding, significantly reduced waiting times and increased operational efficiency in critical sectors. This is just one example of how advanced technologies can be effectively utilized to optimize processes. The proposed virtual model shows significant improvements in both efficiency and flexibility; however, it has limitations, especially regarding scalability. These limitations must be addressed for further development of the model.