{"id":174820,"date":"2026-10-27T11:52:48","date_gmt":"2026-10-27T17:52:48","guid":{"rendered":"https:\/\/tecscience.tec.mx\/en\/?p=174820"},"modified":"2026-10-28T12:25:51","modified_gmt":"2026-10-28T18:25:51","slug":"artificial-intelligence-in-supply-chains","status":"publish","type":"post","link":"https:\/\/tecscience.tec.mx\/en\/business-innovation\/artificial-intelligence-in-supply-chains\/","title":{"rendered":"How Close are we to Autonomous Supply Chains?"},"content":{"rendered":"\n

A system integrating predictive artificial intelligence<\/a> (AI), mathematical optimization models, and large language models (LLM) was applied to Microsoft’s cloud services supply chain<\/a>, increasing the productivity of workers responsible for planning the purchase and distribution of servers by 20 to 30 percent. Decisions that previously took two and a half days to be made can now be resolved in almost real time.<\/p>\n\n\n\n

This is an example of how AI, LLMS, and agent systems are already helping companies reduce costs, anticipate demand for their products, and make better decisions.<\/p>\n\n\n\n

Traditionally, LLMs \u2014like those used by ChatGPT\u2014 have been associated with tasks such as writing, summarizing, or analyzing texts, but their ability to understand human language can also be leveraged to transform business operations.<\/p>\n\n\n\n

In the eyes of David Simchi-Levi<\/a>, a researcher at the Massachusetts Institute of Technology<\/a> (MIT) and Purdue University<\/a>, these models are excellent intermediaries between workers and the complex mathematical systems that companies use to make decisions.<\/p>\n\n\n\n

\u201cWe can train them to translate from the language of the planner to the language of the technology and back from the technology to the planner,\u201d Simchi-Levi said during his plenary talk, Supply Chain and Business Transformation with AI and Large Language Models<\/em>, at the event From Resilience to New Opportunities: Building the North American Supply Chain of the Future<\/a><\/em>, organized by Tec de Monterrey and the National Academy of Engineering<\/a> (NAE).<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
David Simchi-Levi, a researcher at the Massachusetts Institute of Technology (MIT) and Purdue University, and a member of the National Academy of Engineering (NAE). (Photo: Tecnol\u00f3gico de Monterrey)<\/figcaption><\/figure>\n\n\n\n

Currently, supply chains face increasing challenges, such as geopolitical instability, trade uncertainty, labor shortages, and rising costs of materials, energy, and transportation.<\/p>\n\n\n\n

These problems are forcing companies to seek greater efficiency, resilience, and productivity through technologies like these. However, while the advances are promising, significant obstacles remain, such as their reliability.<\/p>\n\n\n\n

LLMs to Explain Complex Mathematical Decisions<\/h2>\n\n\n\n

To improve a company’s operations, an optimization system might recommend buying a specific product from a particular supplier, but if the person responsible for purchasing doesn’t understand why, they might ignore the recommendation.<\/p>\n\n\n\n

Thus, LLMs allow workers to ask questions in everyday language and receive understandable explanations.<\/p>\n\n\n\n

Since 2017, Simchi-Levi and his team have worked with companies across various industries to transform their processes using machine learning, predictive models, and mathematical optimization. In recent years, they have incorporated generative AI to automate some of these processes and make them easier to use.<\/p>\n\n\n\n

“This leads to a fast and data-driven decision-making, it provides a lot of explainability and interactive process with the technology, and it leads to automation,” the expert explained.<\/p>\n\n\n\n

At Zalando, a European e-commerce company, they used predictive models and optimization to adjust the prices of approximately six million products in 23 countries and recommend alternatives when popular items run out.<\/p>\n\n\n\n

At a pharmaceutical company, employees began to follow AI recommendations more closely once the system explained how it arrived at them. AI usage increased from 22% to 83%, and the company managed to reduce its inventory by approximately 5%.<\/p>\n\n\n\n

These examples show that the potential of LLMs is not necessarily in replacing existing tools, but in making their recommendations easier to understand and use.<\/p>\n\n\n\n

Towards Autonomous and Reliable Supply Chains<\/h2>\n\n\n\n

The next step \u2014and the dream of many\u2014 would be to develop systems capable of making decisions and adapting their strategies without relying on direct human intervention, resulting in completely autonomous supply chains.<\/p>\n\n\n\n

To assess how close we are to achieving this, Simchi-Levi and his team developed an experimental platform that compares the decisions of AI agents with those of people managing simulated supply chains.<\/p>\n\n\n\n

The results showed that some reasoning models managed to reduce costs by approximately 30% compared to humans. By incorporating a coordinating agent, called an orchestrator<\/em>, the reduction reached around 50%.<\/p>\n\n\n\n

The problem was that although the agents obtained good results on average, their decisions could vary considerably even when faced with the same conditions.<\/p>\n\n\n\n

In some experiments, this inconsistency increased over time and was amplified across different links in the supply chain.<\/p>\n\n\n\n

\u201cWe have a technology that is doing well on average, but is highly unreliable,\u201d Simchi-Levi pointed out.<\/p>\n\n\n\n

To improve their reliability, the researchers tried training the models with specialized knowledge and incorporating constraints into their decisions. Both strategies produced improvements, but were complex to implement.<\/p>\n\n\n\n

Their findings suggest that an AI capable of making economically efficient decisions is not necessarily a reliable one.<\/p>\n\n\n\n

The challenge to achieve autonomy lies not only in developing smarter models, but in ensuring that their decisions are consistent, understandable, and appropriate even when conditions change.<\/p>\n\n\n\n

Although it was not addressed during the conference, the fact that they are becoming increasingly autonomous reinforces questions about who should supervise their decisions and take responsibility when mistakes are made.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Did you find this story interesting? Would you like to publish it? Contact our content editor for more information: marianaleonm@tec.mx<\/a><\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

David Simchi-Levi, an MIT researcher, explains how AI and language models are transforming business operations and what is still needed to achieve fully autonomous supply chains.<\/p>\n","protected":false},"author":12,"featured_media":174823,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[92],"tags":[595],"class_list":["post-174820","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-innovation","tag-national-academy-of-engineering"],"acf":[],"yoast_head":"\nAI in Supply Chains: Advances and Challenges | TecScience<\/title>\n<meta name=\"description\" content=\"Artificial intelligence is already transforming the supply chains of companies like Microsoft, but it still faces reliability challenges.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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