Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools

The paper focuses on the identification of “bottlenecks” in the passenger handling process at the airports. In the current era of digital transformation and the emergence of Industry 4.0 and 5.0 concepts, optimizing passenger flows through data-driven tools is becoming an essential part of intelligent airport management. While many solutions focus on high-end software or AI-based systems, this study demonstrates the value of preparatory models built in widely accessible platforms such as Microsoft Excel. A simulation model was developed to analyze check-in and security screening, integrating discrete event simulation (DES), queueing theory, and elements of Monte Carlo simulation. The model enables the segmentation of the handling process into key events, including probabilistically generated arrivals and service durations. Although the model is built in a basic environment, it serves as a prototype platform for potential integration into broader digitalization strategies, offering a preparatory framework for future implementation in more sophisticated systems.