Ocean freight remains pivotal to global trade, with a staggering 90% of all consumed goods transported by sea. This sector, however, is not immune to disruptions, resulting in significant freight pricing volatility that ultimately affects consumers through shortages or price fluctuations.

Economic factors such as slow growth, a recession, and supply variations, can all influence ocean freight rates. Lately, we’ve seen geopolitical factors play a more significant role. Notable events like the COVID-19 pandemic, the Suez Canal blockage, the conflict in Ukraine, and the Red Sea attacks are all clear examples. A specific incident highlighted by the Freightos Baltic Index occurred in early 2024, where ocean spot pricing surged by 80% within just eleven days following Houthi attacks in the Red Sea— affecting not just regional rates, but also causing a worldwide ripple effect.

Predictive analytics is emerging as a key tool in the ocean freight sector. These technologies utilise historical data, and real-time inputs like weather conditions and geopolitical shifts to forecast the potential impact of disruptions along major shipping routes. Despite these capabilities, recent events like the Baltimore bridge collapse and the earthquake in Taiwan demonstrate the limits of predictability. For example, in the days after the Baltimore incident, a lot of focus was placed on identifying stranded cargo and rerouting supply chain routes rather than on studying, analysing, and adjusting shipping costs.

Historically, freight rates have been determined using customer demand, fuel costs, and vessel availability. However, the unpredictable nature of global crises introduces complexities that have made the traditional methods redundant. Amid these challenges, artificial intelligence (AI) presents a transformative opportunity to mitigate the long-term impact of disruption on ocean freight pricing.

AI freight pricing advantages

AI can work in real time to understand the market reaction to volatility and help shipping companies return to optimal pricing conditions. Through continuous monitoring of various data sources, AI systems can quickly detect changes in shipping conditions and alert stakeholders to potential disruptions. This rapid response capability enables shipping companies to adjust strategies promptly—whether by rerouting cargo or adjusting pricing—to mitigate economic impacts swiftly. In recent years, highly data-efficient and decision-making algorithms have been developed to study historical pricing activity (booked business) to best support shipping companies with their freight pricing strategy.

Of course, black swan events can very much throw the industry off and demonstrating the resilience of advanced models becomes a paramount need. AI has become so sophisticated that even in once-in-a-lifetime events, models can help shipping companies return to optimal selling rates up to 30% quicker than existing systems have traditionally done. This can represent millions of dollars to a freight forwarder’s top line.

AI’s real-time data processing significantly outpaces human capabilities, providing a critical advantage in high-pressure and mission-critical scenarios. What makes pricing models so complete is their ability to process huge amounts of data, and evaluate multiple trade-offs even in environments where data is scarce. A mere five data points per week on a given route (e.g. Shanghai to Hamburg), is all the best-in-class AI models will need to come up with optimal and reliable freight pricing recommendations.

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Link to article: https://techmonitor.ai/comment-2/ocean-freight-pricing-time-to-embrace-ai