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Global Trade Management

AI and supply chains: The future is (almost) here

Tad Simons  Technology Journalist/黑料大湿Posts Institute

· 5 minute read

Tad Simons  Technology Journalist/黑料大湿Posts Institute

· 5 minute read

As the use of artificial intelligence and other forms of machine learning becomes more commonplace, the impact on global supply chains will be profound

Various forms of machine learning and automation have for years been changing how supply chains operate, and these changes have generally made global commerce more efficient and resilient, especially for the companies that have implemented them.

However, the rapid development of increasingly powerful forms of artificial intelligence (AI) promises to do more than just improve upon previous generations of technology. Indeed, so-called generative AI 鈥 the kind used in OpenAI’s popular chatbot ChatGPT 鈥 has the potential to significantly alter, if not revolutionize, how supply chains are managed and organized.

More powerful tools

Over the next few years, developers of trade-management software will be incorporating more AI-powered tools into their products, and users will begin discovering what these new tools can do. In some cases, the improvement will look like a dramatic expansion of current features and capabilities, enabling better forecasting and much more granular visibility into all aspects of the chain. In other cases, AI-assisted supply-chain analysis may open up possibilities no one could have imagined before, yielding creative new insights and strategies that existing procedures could not reveal.

Data is the key to this brave new world of supply-chain management. Software engines powered by generative AI can process much larger sets of data than previous forms of machine learning, and the neural networks through which the data travels are designed to act much like a human brain, albeit one capable of analyzing an almost infinitely complex set of variables. Generative AI can also learn 鈥攁nd teach itself 鈥 about the nuances of any given company’s supply-chain ecosystem, allowing it to refine and sharpen its analyses over time.

Superior supply chains

What might these new capabilities allow supply-chain professionals to do? Here are a few tantalizing possibilities:

Forecasting and risk management 鈥 Generative AI engines can process a much broader spectrum of information than current machine-learning models and use real-time data to create more accurate and timely planning scenarios. Almost any variable affecting a company’s production and shipping can be factored in 鈥 weather patterns, crop yields, power outages, labor issues, port congestion, manufacturing delays, materials shortages, component prices, and much more.

Real-time predictive analytics of this kind (rather than analytics based on historical data) can help companies anticipate potential supply-chain bottlenecks much further in advance, formulate up-to-the-minute contingency options, and respond more nimbly to fluctuations in supply and demand.

Inventory management In addition to knowing exactly where everything is across the entire supply chain, AI-enabled tools can potentially turn inventory management into a profit center by extracting efficiencies gained by deeper analysis of inventory data. This might mean optimizing inventory levels more efficiently, or using price and tracking data to move products to locales where they are more valuable or finding the most effective way to manage free- and foreign-trade zones.


Software engines powered by generative AI can process much larger sets of data than previous forms of machine learning, and the neural networks through which the data travels are designed to act much like a human brain, albeit one capable of analyzing an almost infinitely complex set of variables.


In warehouses, it might mean analyzing traffic patterns and product movement to determine the most efficient use of space, or unlocking workflows that avoid unnecessary wastes of time and energy.

Transportation logistics 鈥 Generative AI algorithms can take a much more expansive view of the supply chain, allowing supply-chain managers to optimize transportation and delivery options based on real-time traffic patterns, weather conditions, and other variables. Such systems can also reduce vehicle down-time by identifying the most efficient way to maintain cars, trucks, ships, and planes 鈥 and lowering costs by locating the most cost-effective fuel options at any given point in time.

Companies concerned about Environmental, Social & Governance (ESG) compliance can also use generative AI to reduce their carbon footprint by analyzing the company’s entire product ecosystem to best identify the cleanest, most fuel-efficient ways to transport goods around the world.

Product and process development 鈥 When developing products, generative AI can propose design solutions and advise companies how to manufacture and distribute a product in the most efficient possible way. Materials, sourcing, manufacturing processes, cost constraints, pricing, sustainability, resilience, and more 鈥 generative AI can take all of these factors and into account and point the way toward a process that may yield significant savings up and down the supply chain.

Economies of scale 鈥 The name of the game in supply-chain management is optimization, efficiency, and cost-containment. One of the most appealing prospects of using generative AI in a business context is that it can be used to leverage ever greater economies of scale, especially in large corporations. The magic sauce in this technology is that it can analyze data from all parts of a company, identifying better ways to reduce costs, spread risk, and shave efficiencies in ways no one had thought to try before.

The future of supply-chain management

Some machine-learning tools are already performing tasks similar to those discussed above; what future iterations of generative AI promise is vastly superior performance and an ever-expanding, ever-improving menu of options for leveraging company data to gain a competitive advantage.

Admittedly, the possibilities proposed here require an advanced, networked technological infrastructure that many companies simply don’t have or are still in the process of developing. And yes, it can be a long process. But the possible uses of generative AI in supply-chain management and elsewhere should prompt some serious discussion about the benefits these new AI-enabled tools can provide and how they might be integrated into a company’s processes and culture.

Controlling every aspect of a supply chain is impossible, of course, but generative AI could bring companies several steps closer to the sort of resilient, sustainable, cost-effective supply chains that the future will require.

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