The container tracking data is crucial for the effective management of supply chains. In
this report, we analyze container tracking data to identify areas for improvement in supply chain operations. Our study aims to provide insights into the factors affecting container movements,
identify areas where delays and bottlenecks occur, and suggest ways to optimize operations. The supply chain is a complex system involving multiple parties, including shippers,
freight forwarders, carriers, ports, and customs agencies. The timely delivery of goods is critical for maintaining customer satisfaction and reducing costs. Therefore, it is essential to have a robust tracking system that enables the monitoring of container movements and identification of any issues that may arise.
To achieve these objectives, we used the CRISP-DM (Cross-Industry Standard Process for Data Mining) process, a widely used framework for data analysis. The CRISP-DM process involves six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. We used this framework to analyze container tracking data and
identify opportunities for improving supply chain operations.
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Al-Ali, Omran, "Shipment Containers tracking optimization using Machine Learning" (2023). Thesis. Rochester Institute of Technology. Accessed from