PORT AUTHORITIES & TERMINAL OPERATORS

In port operations, port authorities and terminal operators serve distinct but financially interconnected roles. Terminal operators are commercial entities responsible for the day-to-day handling of cargo within specific terminals. In contrast, port authorities act as landlords and regulators, often publicly-owned, overseeing the port's strategic development. 

The financial relationship is layered: container shipping lines pay terminal operators for cargo handling services, and terminal operators, in turn, pay concession fees to port authorities. As a result, port authorities have a vested interest in ensuring the economic vitality of the port, as their revenue depends on healthy and efficient terminal activity.

CONVERGENCE OF MARKET AND CONTRACT RISKS

Terminal operators and port authorities are bound by long-term contract offtakes (often 10 to 30 years) that define operational and financial responsibilities. The key financial link, concession fees, may be reassessed annually based on factors like inflation and trade volumes. Yet during periods of economic shock, these contract offtakes start to resemble market offtakes, with revenues increasingly tied to volatile supply and demand. While long-term contracts aim to stabilize risk, market disruptions reveal how contractual and market risks can begin to converge, blurring the lines between predictable returns and market-driven exposure.

Another example of this convergence between contract and market risk can be seen in energy offtake agreements for bunkering infrastructure, where port authorities or shipping companies secure long-term fuel supply. The Net-Zero IMO (International Maritime Organization) Framework, to be formally adopted in 2025, will mandate the maritime industry to reach zero-emission fuels by 2050. As a result, port authorities now face critical decisions around energy security and infrastructure investment in an industry where oil & gas account for nearly 80% of total bunkering fuel today. 

However, beyond IMO regulations, two key factors also influence energy offtakes. First, during major shocks, demand often falls as prices rise and inflation pressures escalate. Second, fuel prices become highly volatile in times of economic instability. In both cases, long-term fuel needs become harder to predict, exposing energy offtake agreements to greater market risk.

Given the multitude of variables at play, demand prediction will never be completely accurate, not even with AI. Trade volumes in port operations have been forecasted using historical data, economic indicators like GDP, and industry trends. But today’s reality is far more complex. In a world where economic conditions, energy security, and climate risks are increasingly unpredictable, the question becomes: how do we navigate and adjust to uncertainty?

AI AND PORTS

While still an emerging field, AI integration in port operations falls broadly into two categories:

• Machine learning uses historical data to find patterns and make predictions. For example, ports can analyze past data—such as tariff rates, seasonality, fuel prices, and economic indicators—to forecast monthly trade volume at a terminal.

• Scenario simulations, particularly Monte Carlo simulations, use probabilistic sampling from input distributions that represent the ranges of possible values of each variable to model thousands or even millions of possible outcomes. By systematically analyzing the spread of modeled outcomes, ports can estimate how trade volume might shift across a range of market, climate, and geopolitical conditions.

However, when there is high uncertainty, calculating demand gets tricky. Sensitivity analysis helps by quantifying how changes in one variable affect the outcome. For example:

“How much bunkering fuel volume would we need annually to break even on a $50M investment?”

While pricing relationships may seem chaotic, a basic accounting truth holds: money flowing in one place flows out somewhere else. Looking at these flows through a financial lens helps identify where systems break down and where they remain resilient.

Advanced AI models enable more creative and flexible problem-solving by redesigning how the processes mentioned above can be combined and applied to new challenges. This is especially valuable in volatile markets responding to immediate disruptions or planning for long-term contracts and infrastructure investments. Taking our sample use case above, we can approach the problem from two distinct perspectives:

1. Choose a “Node” 

With this approach, we start by identifying key nodes that each represent an input in port operations (fuel prices, operating expenses, the investment timeline, etc.). By assigning different values to these nodes, we can simulate real-world scenarios involving uncertainties like rising fuel costs, regulatory changes, or demand fluctuations, and assess how these factors impact the breakeven fuel volume.

These insights can help decision-makers identify the conditions under which an investment is viable or at risk and respond quickly with strategies like price adjustments or resource reallocation. Rather than relying on fixed assumptions, this approach keeps ports responsive and resilient amid both long-term uncertainty and short-term disruption.

2. Backsolving

Backsolving starts with the end goal of breaking even on a $50 million investment and works backward to determine how much bunkering fuel volume is needed annually. Ports can set constraints on values for cost margins, OPEX, and fixed expenses, to limit the search space over which the model will probe to identify the minimum fuel volume required. This analysis can also highlight where certainty in the value of these variables matters most, allowing ports to focus on the most sensitive levers in the equation.

While the examples provided are simplified, real-world modeling is far more complex—but that’s where AI shows its true potential. Refined and more elaborate AI models can process and integrate data across sectors, enabling models that combine economics, capital planning, and regulatory oversight in high-risk environments.  The real question is: when the next wave of shock hits, will you battle the wave or be ready to ride with it?