## 2 Materials and Methods
### Factors Influencing the Selection of a Simulation Tool  
Selecting the right simulation software is a critical step in effectively modeling and analyzing manufacturing systems. The choice should be guided not only by general functionality but also by how well the tool aligns with the specific nature of the modeled process in this case, the selective soldering process. A range of simulation platforms is available on the market, including general-purpose environments such as AnyLogic, SIMUL8, SIMIO, and FlexSim, each offering different strengths. However, not all of them provide the same level of specialization and user friendliness when applied to complex industrial processes. AnyLogic is a flexible simulation platform that supports multiple modeling approaches, including discrete-event, agent-based, and system dynamics. It is well suited for hybrid models and large-scale strategic simulations, particularly in research and multi-disciplinary projects. However, its general-purpose nature often requires more extensive customization to accurately model detailed industrial operations. SIMUL8 is known for its simplicity and ease of use. It is commonly applied in service systems and healthcare process modeling. While its intuitive interface is accessible to beginners, SIMUL8 may be limited in terms of advanced features, detailed 3D visualization, and integration with external engineering tools, making it less suitable for complex manufacturing processes. SIMIO combines object-oriented modeling with a visual process design environment. It offers strong support for scheduling and real-time simulation, and it is particularly useful for capacity planning and operational decision support. However, its learning curve and license structure can be challenging for smaller or highly specialized projects. Among these tools, FlexSim stands out as one of the most advanced simulation environments, specifically developed for manufacturing and logistics systems. It offers a unique combination of usability, modeling depth, and industry-relevant features that make it especially effective in real-world production applications [32; 33]. The decision to use FlexSim in this study was based on both its proven track record in industrial use cases and its rich feature set. A number of recent studies confirm the practical value of FlexSim in optimizing complex production systems:  
* In a pharmaceutical production study, FlexSim was used to model tablet manufacturing under various shift systems. The simulation enabled campaign time reductions of 50-53% and labor savings of 9-14%, without affecting actual operations [34]. * In a filling machine optimization project, FlexSim helped implement a mathematical model that allowed simultaneous multi-flavor yogurt filling, significantly reducing process time and increasing flexibility [35]. * Another study applied FlexSim to identify system bottlenecks and evaluate production layout redesign. The simulation revealed critical inefficiencies and supported improvements in workforce utilization [21]. Additionally, a Polish case study demonstrated that FlexSim effectively models complex production logistics in 3D and facilitates optimization of throughput and resource allocation [36]. * In the logistics domain, FlexSim was used to optimize material replenishment processes. The integration of lean manufacturing principles led to a measurable increase in operational efficiency [37]. Additionally, simulation has been successfully applied to optimize PCB manufacturing processes, where detailed models of production lines enabled better decision-making in layout design and process planning [33]. Beyond its empirical success, FlexSim offers a number of technical advantages that make it particularly suitable for industrial process modeling:  
* Seamless data integration from Excel and CAD programs (e.g., AutoCAD 2024, SketchUp), allowing rapid updates and compatibility with existing engineering documentation.