Project S02 Freight Tracking and Analytics

A project that aims to optimise marine transportation by integrating real-time data into a user-friendly interface, empowering stakeholders with insights for efficient vessel management and strategic decision-making.

Introducing Project S02 Freight Tracking and Analytics

The project aims to enhance the monitoring of marine transportation by Aramco through its fleet of time-chartered vessels. By employing analytics, the initiative seeks to provide users with insights into vessel positions, port status, and refinery outputs. This will empower decision-makers with the data needed to optimise vessel profitability, streamline operations, and make informed strategic decisions regarding vessel positioning and chartering. The focus is on leveraging freight analytics to ensure safe, efficient, and cost-effective maritime transport operations, aligning with the company’s goals of operational excellence and sustainability.

Team members

Alvin Joseph (ISTD), Kartik Nistala (CSD), Jobelle Lee Ern Ee (CSD), Kenny Ong Ker Chin (CSD), Lin Lin (ESD), Seah Wan Xuan (ESD)

Instructors:

Zhao Na

Writing Instructors:

Bernard Tan Chee Seng

Maritime Efficiency Through Integrated Analytics

Our solution harnesses integrated analytics and predictive models to elevate maritime shipping efficiency. By unifying vessel, port, and refinery data within a singular, streamlined platform, we facilitate immediate access to crucial operational parameters, such as weather conditions, vessel locations, and specific voyage details. This cohesive integration addresses the inefficiencies arising from fragmented tracking systems, leading to improved strategic routing and decision-making.

Leveraging historical data analysis, our platform delivers key insights into regional freight rates, presenting predictive forecasts that empower the company to proactively manage short-term fluctuations. This innovative approach not only elevates operational performance but also ensures a future where maritime logistics are more predictable, efficient, and aligned with the dynamic needs of global trade.

Optimized for Performance and Scalability

An innovative web application powered by PostgreSQL for robust data management, Python for dynamic backend scripting, and Golang for lightning-fast API web services. Utilizing npm as the package manager and Node.js as the frontend web server, this project ensures seamless user interactions. Built on the React.js framework, expect a modern and intuitive user interface. Backed by the reliability and scalability of Google Cloud Services, this application promises a secure and high-performance experience for users worldwide.

Explore Our Web Application!

Our web app offers intuitive exploration of maritime elements with an interactive map. Detailed info, bookmarking, fast search, and filtering streamline navigation. Users can track vessel routes, visualize weather, and predict freight prices, empowering informed decision-making.

Features

Map Visualisation

Effortlessly explore vessels, refineries, and ports on a map. Hover over icons for detailed information. Navigate seamlessly across areas and adjust focus with zoom options.

Information Sidebar

Access detailed information about entities like vessels, refineries, and ports by clicking on icons, revealing a sidebar with comprehensive data and key graphs for deeper insights.

Bookmarks

Organize and access preferred content easily, enhancing the browsing experience by managing and revisiting essential information conveniently.

Search & Filter

Fast and effective way to find specific vessels, refineries, or ports, offering real-time results and advanced filtering options for efficient data exploration.

Vessel Route

Historical trajectories on the map interface allow users to analyze movement patterns and journey progression.

Real-time Weather Visuals

View live forecasts and overlay additional weather data, including wind direction, precipitation intensity, and past weather conditions using a slider for up to 6 hours prior.

Freight Price Prediction

Utilisation of predictive models for estimating short-term freight pricing for different trade routes and vessel classes considering a variety of historical features

Uncover the full potential of our application.

In partnership with :

Supported by :

Acknowledgements

We extend our sincerest appreciation to Tejas, Shu Ting, Prashant, You Wei, and the rest of the Aramco Trading (Singapore) team for their collaboration. We are also grateful to our capstone mentors, Professors Zhao Na and Soujanya, for their invaluable guidance. A special thanks to our CWR mentor, Professor Bernard, for his mentorship and insights throughout this journey. Their combined efforts have been a cornerstone of our learning and success.

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Team members :

Alvin Joseph

Information Systems Technology and Design (ISTD)

Kartik Nistala

Computer Science and Design (CSD)

Jobelle Lee Ern Ee

Computer Science and Design (CSD)

Kenny Ong Ker Chin

Computer Science and Design (CSD)

Lin Lin

Engineering Systems and Design (ESD)

Seah Wan Xuan

Engineering Systems and Design (ESD)