# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 5 Conclusions  
The presented simulation model contributes to the analysis of passenger handling processes at airports by enabling the simulation of fixed configurations with the possibility to modify input data, such as the number of passengers, departure times, passenger behavior characteristics, or service duration. This flexibility in input settings allows for the analysis of various operational scenarios and the comparison of their impact on passenger handling efficiency.  
Unlike models described in the literature, which often focus on individual parts of the process or assume simple FIFO (first-in, first-out) assignment, this model covers the entire passenger handling process, providing a more comprehensive view of the passenger flow.  
Although it is based on assumptions and cannot account for non-standard situations or real-time dynamic changes, the model delivers accurate results for fixed configurations and allows for the identification of bottlenecks in the process. Compared to advanced software solutions requiring specialized tools and technical expertise, this model offerssimplicity and accessibility, making it especially suitable for smaller airports with limited technological capacity.  
More importantly, this model should not be seen as a replacement for advanced simulation platforms but as a steppingstone in the digital transformation of airport operations. In the context of Industry 4.0 and the emerging human-centric and sustainable principles of Industry 5.0, the model represents a preparatory platform as a prototype tool that enables airports to test and analyze process designs before investing in fully integrated digital systems.  
Its adaptability also supports the gradual introduction of innovative technologies, such as self-service kiosks, biometric control, or automated decision-making systems, by providing a clear baseline and comparative data. With the ability to repeat simulations using the Monte Carlo method, the model generates statistically robust outputs for various operational conditions, enabling evidence-based optimization decisions.  
In conclusion, the developed model not only provides a practical tool for the analysis and optimization of passenger handling processes at airports but also represents a flexible and accessible platform for digital prototyping. It demonstrates how even widely available tools like Excel can be effectively used within the broader framework of aviation digitalization and serve as valuable support in the adoption of innovative technologies.  
Conceptualization, E.J. and T.G.; methodology, T.G.; software, T.G.; validation, E.J., M.J. and P.H.; formal analysis, P.H.; resources, M.J.; data curation, M.J.; writing--original draft preparation, T.G.; writing--review and editing, E.J.; visualization, E.J. and T.G.; supervision, M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.