# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 4 Discussion  
The simulation model developed for airport handling processes, including check-in and security screening, provides a valuable tool for analyzing the efficiency of handling processes and identifying potential bottlenecks. By capturing key operational metrics such as total time in the system, queue waiting times, and service station utilization, the model enables detailed performance assessment under varying conditions. One of its main benefits lies in its ability to detect critical congestion points, moments, and locations within the process where delays accumulate and service efficiency drops. The model also enables the simulation of various operational scenarios. Through ten modifiable input parameters, the system can reflect changes in passenger behavior and infrastructure configurations. These include the scheduled departure time, total number of passengers booked, average and standard deviation of arrival time at the airport, average and standard deviation of walking time to the check-in area, average check-in service time, and four separate average service times for each security scanner. These parameters can be easily adjusted in the Excel environment, allowing for the simulation of peak periods, differences in passenger composition (e.g., business vs. leisure travelers), or variations in service quality at individual stations. Since all input data are tied to probabilistic logic, even small adjustments can result in statistically significant variations in outputs, making the model useful not only for planning but also for sensitivity analysis and testing alternative strategies. A particularly promising extension of this model involves the use of Monte Carlo simulations. This approach consists of repeatedly running the simulation (potentially thousands of times) with different sets of random input values generated from defined probability distributions (e.g., normal, exponential). In Microsoft Excel, this can be automated using VBA macro programming, which allows for the batch execution of simulation runs, dynamic input parameter variation, and automatic collection of output results. Each iteration would record key system metrics (such as average time in the system, queue lengths, and waiting times), which can then be statistically processed and visualized using built-in charting tools. For example, changes in a single parameter, such as average passenger arrival time, could be tested over a defined range of values. The macro would automatically recalculate the simulation for each variation, store the resulting performance indicators in a summary table, and optionally generate comparative graphs to illustrate system behavior across scenarios. This capability allows researchers or airport planners to evaluate a broad spectrum of \"what-if\" situations, forecast performance under uncertainty, and derive more robust, evidence-based decisions. If variability is additionally introduced directly into the input parameters (e.g., defining intervals for arrival behavior or scanner speed), the model could simulate complex operational conditions and monitor performance distributions over a wide range of inputs. Excel also offers the possibility of creating various configurations of the simulation model for handling processes, which can be expanded to include different methods of assigning passengers to service stations and incorporating new types of handling processes. In addition to the already implemented dynamic assignment based on station availability or cyclic assignment ensuring even distribution of passengers among stations in a fixed order, the model can be expanded to simulate passenger behavior in selecting shorter queues. This method captures the natural tendency of passengers to join shorter queues, potentially leading to more efficient station utilization. While this simulation does not fully replicate the behavior of all passengers, it provides an opportunity to explore how such a strategy would affect the smoothness of the handling process.