# Optimizing Flight Delay Predictions with Scorecard Systems
## 8 Conclusions  
Scorecards are an innovative tool that significantly improves operational efficiency and service quality in the aviation sector. Their simplicity, flexibility, and ability to integrate multiple factors make this solution highly beneficial for both airlines and airports. By monitoring key performance indicators (KPIs) such as punctuality, ground handling time,and crew availability, scorecards enable a quicker identification of potential issues and the implementation of preventive measures. For instance, in cases of delays caused by adverse weather conditions, scorecards can pinpoint the most at-risk flights, allowing for better resource planning. The simplicity of interpreting scorecard results facilitates data-driven decision-making without requiring advanced analytical skills. This tool is particularly useful in dynamic operational environments, where quick decisions are crucial. Moreover, high punctuality and operational reliability supported by scorecards enhance competitiveness, boosting passenger loyalty and market position. This study aimed to answer the following research question: Can a scorecard-based model provide a simple, interpretable, and effective method for predicting flight delays? The results clearly confirm this. This study demonstrated that scorecards can be used to accurately predict flight delays using historical U.S. flight data. The model achieved high predictive performance (Gini = 94.6%) while maintaining full interpretability, addressing the research goal of developing a simple yet effective forecasting method. Compared to traditional statistical and machine learning approaches, the scorecard method offers a compelling balance between accuracy, transparency, and ease of implementation. Implementing scorecards also leads to a significant reduction in operational costs. Improvements in punctuality and predictability reduce expenses related to additional fuel consumption, crew overtime costs, and penalties or compensation for delayed passengers. Better resource management also results in a more efficient use of airport infrastructure and fleet. Furthermore, proactive delay management ensures that passengers are informed earlier about potential disruptions, minimizing frustration and improving customer satisfaction. The insights generated by scorecards can also be used to create transparent reports, fostering trust and loyalty among passengers. Nonetheless, some limitations remain. The current model relies on historical and operational data but does not yet incorporate real-time external factors such as weather nowcasting or real-time congestion data. Additionally, binning methods used in scorecard construction, while interpretable, may lead to the loss of granularity in some scenarios. Future research should aim to address these limitations by integrating real-time data feeds, incorporating dynamic variables, and testing the model in non-U.S. operational contexts. The inclusion of explainable AI (XAI) extensions may also offer a hybrid solution that preserves interpretability while boosting adaptability. The potential for further research into scorecards is vast. Incorporating artificial intelligence (AI) into scorecard development could enable real-time updates of weights and scores based on live data, supporting the prediction of rare but critical events such as large-scale operational disruptions. Simultaneously, personalized scoring models tailored to the specific needs of different airports and airlines could enhance their effectiveness. Integrating scorecards with operational management systems, such as fleet management or enterprise resource planning (ERP) systems, requires a further exploration of interoperability and data standardization. In the context of sustainability, scorecards could include environmental indicators such as CO2 emissions and fuel consumption, supporting airlines in achieving their environmental goals.