# Predictive Analysis for Optimizing Port Operations
## 5 Discussion  
From the results, tree-based methods exhibit the best performance, particularly in predicting Total Time and Delay Class. By providing tools to accurately predict and classify vessel behavior, this research contributes greatly to improving our understanding of various methods, which can be used to make more efficient and effective predictive analytics-based decision-making in port management. The proposed solution has wide-ranging applications and integration into different aspects of maritime logistics, from ship scheduling to service delay predictions to risk assessment, demonstrating its potential to enhance overall port performance and optimize supply chain operations.  
Additionally, the incorporation of feature analysis through feature importance enriches our understanding of the factors influencing maritime logistics, where predictions are influenced by Berth and Cargo Characteristics features while classification tasks are impacted by Geographical features (Total Time and Delay Time) and operation characteristic features (Delay Time). This offers insights that can be leveraged to further improve the efficiency of port operations and make them more resilient. Since Berth plays a key role in the prediction of Total Time and Delay Time, using a Top-Bottom approach for developing individual models for ports and then berths would be beneficial in understanding how these ports and berths can be made more efficient.  
SHAP (Shapley Additive Explanations) analysis plays a crucial role in this process by helping us understand how these features contribute to the outcomes, providing deeper insights into each individual case. This allows for the identification of specific mitigation steps that can be taken accordingly. Features like Berth, Source, Destination, and Month usually come out as key features impacting outcomes. Learning from this, a suggestion for an optimal Berth-Source-Destination combination, along with travel month, can be made and checked to see if it is operationally feasible. SHAP helps us address the predictive analytics of port operations at both the global and local levels, ensuring that the models are both comprehensive and precise.  
Overall, this approach, enhanced by Feature Importance and SHAP analysis, contributes to a more nuanced understanding of port logistics and supports the development of more effective and resilient port management strategies, which can help with optimizing the Berth Allocation Problem (BAP), Predictive Model Scheduling (PMS), and port risk assessment. By enhancing these aspects, ports can achieve greater sustainability, aligning with the goal of promoting green practices.  
The current set of features is limited, and there is a huge potential for performance gain by accounting for additional variables of maritime logistics. It could involve integrating various other data sources, such as weather forecasts, vessel tracking, and other vessel information, to enhance the accuracy, responsiveness, and scope of the models. These expanded features will lead to enhanced feature analysis as well.  
Another observation is that Deep Learning methods do not currently perform well due to the abundance of categorical data. In the future, the authors plan to address this issue by exploring alternative methods for handling categorical data, such as binary encoding, deep embedding (Cat2 Vec), and other techniques, to see if they can improve the performance of deep learning models.  
Figure 21: SHAP force plot for Delay Class of a sample observation.