After three years of unprecedented trade disruption, big language models and generative AI have emerged just in time to help governments and businesses manage the world's complex supply chains.

“In the coming years, we will see more accurate predictive and predictive analytics, driven by integrated data from every step of the supply chain,” said Julie Gerdeman, CEO of supply chain risk assessment firm Everstream Analytics. “Automated decision-making will reduce risk and disruption, creating resilient, resilient, and adaptive supply chains.”

Better data

Analyzing trade data is an incredibly complex task. These unstructured datasets, consisting of hundreds of millions of shipment records, are scattered across countless subsidiaries and freight forwarding services, making processing and sorting error-prone and labor-intensive.

For example, private trade data companies can use machine learning tools to recognize customs declaration patterns, scan legal documents, and translate languages ​​to create clear, accurate trade data that is easy to search and analyze.

Private trade data companies like Scottsdale, Arizona-based ImportGenius use machine learning tools to recognize customs patterns, scan regulatory documents and translate foreign languages ​​to create clear and accurate trade data that is easy to search and analyze.

“We are building a language learning model to act as an antenna to detect, recognize and incorporate indicators into our platform,” said Paulo Mariñas, CTO of ImportGenius, an Arizona-based commercial data company.

Meanwhile, multinationals like Nestle SA are using AI tools to increase efficiency and detect emerging issues in their global value chains. The Swiss-based food and beverage company uses algorithms to detect product quality issues and ensure self-regulation and control over its production lines.

Mercedes-Benz Group AG is using an AI-powered platform called Omniverse to make its manufacturing and assembly plants more agile. Omniverse helps the German automaker quickly restructure its factories to adapt to external supply shocks.

AI is expected to disrupt many industries, with particularly high growth in trade. This is because the first half of the last decade of globalization was largely about reducing barriers to goods, services, and investment. Meanwhile, the next phase and the current context, the trend of deglobalization, tariff barriers, and geopolitical friction will become a huge challenge for even the most experienced logistics team.

Supply chain analysis

One area where AI applications can have a big impact is in helping companies and governments better understand changes in global value chains.

Last month, G20 trade ministers adopted a framework to map new data, identifying supplier concentration, trade linkages, market volatility and vulnerability of globally important industries.

The idea, announced last week, is to help governments assess the resilience of supply chains and develop measures to mitigate external shocks. The G20 also launched a new AI tool that matches trade data with predictive algorithms, helping policymakers and businesses optimize their export strategies.

AI tools can reduce the time and research required to conclude trade agreements, as well as quickly calculate tariffs on shipping goods. But the complexity and some aspects of international trade policy simply cannot be handled by AI.

“AI can help negotiators prepare better, but it cannot replace real negotiations where the human element is paramount,” said Wendy Cutler, vice president of the Asia Society Policy Institute. “Listening and processing what your negotiating partner is actually saying, reading body language, and coming up with friendly ideas on the spot to bridge differences are things technology cannot do.”

(According to Bloomberg)