Strictly for students

Research Question

How can we develop a predictive model for agricultural commodity prices that enhances global food supply chain sustainability by integrating shipping data, supply analysis and weather conditions?

Problem description

The global food supply chain is a complex and interconnected system, heavily reliant on the efficient shipping of key agricultural commodities such as wheat, corn, barley, soybeans, and soybean meal. Disruptions of the shipping markets (e.g. Russian/Ukrainian war, low water in Panama or the attacks in the Red Sea) imply fluctuations in the prices of these commodities can have far-reaching impacts on sustainability and food security worldwide.

Impact

A predictive model aims to provide insights into commodity price movements. This will enable stakeholders—including farmers, buyers, sellers, policymakers, and shipping companies—to make informed decisions that enhance the efficiency and sustainability of the global food supply chain. Improved price prediction can lead to better resource management and more resilient food systems, contributing to global food security and sustainability.

Research Question

How can we develop an algorithm to accurately detect and map the real-time loading and discharging activities of large vessels carrying food commodities, thereby enhancing transparency and efficiency in global trade and food security?

Problem description

Global trade is the backbone of the world economy and food supply, with millions of tons of cargo being transported by sea every day. However, a significant challenge lies in the lack of transparency regarding which cargoes are being moved at any given time. Currently, information about vessel cargoes is not made available until customs data is released, typically 1-3 months after the fact. This delay hampers the ability of stakeholders to make informed decisions, leading to inefficiencies and missed opportunities.

Impact

This research aims to address this problem by developing an algorithm capable of detecting vessel-voyage activities, down to the berth level. The focus will be on deep-sea vessels with a capacity of over 20,000 deadweight tons (dwt), ensuring that the mapping is relevant and useful for large-scale trade operations. By creating accurate and timely vessel lineups and trade flow maps, we can significantly improve the transparency of global trade in grains and soybeans. This will enable buyers and sellers, policymakers, and other stakeholders to react more swiftly to market changes, optimize logistics, and reduce costs. Ultimately, this project seeks to create a more efficient and transparent global trade system, fostering economic growth and stability.

Research Question

How can the integration of complementary sub-models using statistical and machine learning techniques enhance the accuracy of shipping market price predictions in the face of big data, volatility, and geopolitical uncertainty?

Problem description

The traditional methods of predicting shipping market prices have become obsolete due to big data, market volatility, and geopolitical uncertainty. The aim is to enhance price prediction accuracy by integrating complementary sub-models—such as regional balance sheets, ballast dynamics, destination forecasts, congestion predictions, and dynamic demand data—using advanced statistical and machine learning techniques

Impact

Better models can improve the accuracy of shipping market price predictions. An advancement would address the largest pain point in global markets today, mitigating the limitations of traditional pricing methods that struggle with big data, volatility, and geopolitical uncertainty. Refined models would enable more precise regional balance assessments, better prediction of surplus ship movements, accurate vessel location forecasts, reliable congestion level estimations, and dynamic cargo volume allocations. Such improvements would lead to more efficient shipping operations, better resource allocation, and potentially reduced costs and increased profitability for stakeholders in the shipping industry.

Research Question

How can an integrated prediction model incorporating supply analysis, weather patterns, freight market dynamics, geopolitical events, real-time news feeds, futures market trends, and vessel lineups improve the accuracy of predicting basis premiums in agricultural commodity markets, thereby aiding stakeholders in making more informed hedging and purchasing decisions?

Problem description

Agricultural commodity markets are crucial for managing the supply chain and pricing of essential crops such as wheat, corn, barley, soybeans, and soybean meal. For agricultural end buyers, hedging through futures markets is a primary risk management tool. However, there are critical moments when these buyers must transition from financial hedges to physical purchases. The primary challenge in this transition is accurately predicting the basis premium—the spread between physical market prices and futures market prices.

Current methods for predicting basis premiums are often insufficiently precise, leaving stakeholders exposed to market risks and potentially suboptimal decision-making. This inadequacy arises from the complex and multifaceted nature of agricultural commodity markets, where numerous factors simultaneously influence price movements.

Impact

Developing an accurate prediction model for basis premiums in agricultural commodity markets will enhance decision-making, reduce financial risks, and optimize resource allocation for stakeholders. A good model will leverage advanced statistical and machine learning techniques to integrate various market indicators, providing precise and dynamic predictions. Ultimately, it can contribute to market efficiency, economic benefits, and global food security.

The CM Navigator is a software as a service (SaaS) platform. We see it as an essential tool for professionals working in freight and commodity markets, offering a range of features that streamline risk management, data analysis, and decision-making processes.

As students, you are invited to analyze the unique selling proposition (USP) of this platform, examining what sets it apart from competitors in a highly competitive market. Consider how the platform’s ability to integrate CM Group's complex data sources, provide real-time insights, and offer user-friendly interfaces may appeal to various stakeholders in the commodity industry.

Additionally, assess how CM Navigator enhances operational efficiency and drives smarter decision-making through its advanced analytics and customizable dashboards. Does the solutions on the platform offer unique advantages in terms of product market fit and/or user experience? You are encouraged to explore these dimensions and evaluate whether the platform delivers long-term value for its users. In your analysis, think critically about the problem-solving capabilities of CM Navigator and how it supports its target audience in achieving their business objectives.

(Master Thesis Projects only :-))