# Optimizing Flight Delay Predictions with Scorecard Systems
## 1 Introduction  
Flight delays are a significant issue affecting passengers, airlines, and the entire aviation infrastructure. In an era of increasing globalization and rapid growth of air transport, understanding the causes, consequences, and management mechanisms of delays has become a critical element in improving the efficiency and quality of services in this sector. This study focuses on addressing this challenge by exploring innovative methods to predict and mitigate flight delays, with the aim of improving the operational performance of the aviation industry. The objective of this article is to formulate and assess a novel methodology for predicting flight delays through the use of scorecards. These scorecards function as a streamlinedanalytical instrument that allocates weights to diverse factors affecting delays, thereby establishing an accessible predictive model. In contrast to conventional, intricate techniques, the scorecard system is devised to offer an intuitive and user-friendly solution for airlines and airport operators, reducing the requirement for specialized expertise in data analysis. Delays directly impact passenger satisfaction, a key determinant of airline competitiveness. Punctuality and reliability are among the most valued aspects of air travel, and disruptions can lead to frustration, stress, and missed connections [1]. In addition to inconveniencing passengers, delays entail significant economic consequences. Airlines incur increased operational expenditures, including additional fuel, crew expenses, and penalties associated with service disruptions [2]. Furthermore, delays contribute to environmental concerns by increasing fuel consumption and greenhouse gas emissions during prolonged waiting periods. Efficient delay management can not only improve operational efficiency, but also support the broader goals of sustainability in the aviation sector. Flight delays are influenced by various factors, classified into operational, weather-related, air traffic control, external, passenger-induced, and supply chain disruptions [3]. Operational delays often stem from internal airline processes, such as technical maintenance, the rescheduling of crews, or extended ground services. Weather conditions, such as storms, fog, or strong winds, represent a significant external factor that makes flights unsafe or impossible [4]. Air traffic control constraints, such as congestion in the airspace or on runways, can also result in prolonged delays. Additionally, external disruptions, such as security threats, infrastructure failures, or changes in regulations, as well as passenger behavior, contribute to the complexity of the issue [5]. Traditional delay forecasting methods, including advanced statistical techniques and machine learning models, have demonstrated efficacy but are often resource intensive, requiring specialized expertise and significant computational resources. These approaches, such as ARIMA models, effectively capture trends and seasonality but lack the accessibility needed for a broader implementation [6]. Machine learning methods, though powerful in capturing nonlinear relationships, pose challenges in deployment and maintenance [7; 8]. Consequently, there is a pressing need for simpler and cost-effective tools to address this pervasive issue. To bridge this gap, this research explores the potential of scorecards as an innovative and simplified method for the prediction of delays. Scorecards, widely used in various industries for performance evaluation, offer a structured framework that integrates multiple performance indicators. By assigning weights to critical factors influencing delays, scorecards provide a straightforward predictive model that can be easily adopted by airlines and airport operators without requiring advanced analytical capabilities. The proposed scorecard system aligns with the broader trend toward data-driven decision making in aviation.