Name: Elucidating US Import Supply Chain Dynamics: A Spatio-Temporal Graph Neural Network Approach
Start: 2024-05-17T15:00:00-0400
End: 2024-05-17T15:30:00-0400

Welcome to the MIT SCM Research Symposium 2024 and thank you for joining us! We hope you find the day exciting and informative. To enhance your experience, we are utilizing this platform in order to help identify projects of top interest to you. Below you will find not only the schedule, but also the abstracts and attached executive summaries of each project. Additionally, you can better acquaint yourself with the student presenters and advisors through their profiles. Thank you again and we look forward to hosting you.

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Elucidating US Import Supply Chain Dynamics: A Spatio-Temporal Graph Neural Network Approach

To enhance understanding of congestion points at ports and provide visibility into the incoming goods flow into the USA, this study focuses on maritime ports, using the Port of Boston and New York/New Jersey as case studies. Based on the Automatic Information System (AIS) data, we aim to develop predictive models for port congestion status and the Estimated Time of Arrival (ETA) of container ships. Additionally, we analyze historical commodity flow data to forecast future values, weights, volumes and categories based on Harmonized System (HS) codes. Employing quantitative AIS data analysis provides insights into port congestion dynamics and commodity flow trends, indicating the potential to improve the accuracy of ETA, port management and logistics visibility. This study contributes to both theoretical and practical applications in maritime logistics.

Student Presenters

Nikolay Aristov

Student, CTL MIT

I have more than 15 years of experience in implementing and maintaining SCM software. I performed technical roles on SCM projects, diving into different stages of supply chain planning and execution: demand planning, SRM, WMS, transportation, and master planning. Furthermore, I participated... Read More →