AI Agents in the Supply Chain Ecosystem: The Digital Twin Learns to Talk

End-to-end supply chain optimization is the holy grail of our field. Not only should our own network of factories, warehouses, and distribution centers work together optimally, it must also connect seamlessly with suppliers and customers. The entire ecosystem is included in this scope. The term “supply chain” already implies it: it is a chain, not just a few loose links.

In practice, the ideal of a seamlessly managed chain often seems like a fool’s hope. It is already difficult to make different departments and planning processes within one company communicate effectively, let alone with external partners. Demand, supply, and financial planning each have their own KPIs, priorities, and ways of working. The result is a complex machine whose gears rarely align perfectly.

A supply chain is not a machine but an ecosystem. The difference lies in how its parts communicate. Planning processes don’t exist in isolation; they define conditions for one another. There are many examples of organizations that have already made major progress. For example, HEINEKEN achieves European synergy in its supply chain even though planning was originally done at the country level.

The need for communication does not stop at the boundaries of the organization. Sensitivity around strategic information and competition hinders data sharing, which prevents true end-to-end visibility. KPIs and processes of different partners align even less. The way disruptions are handled also differs between organizations. Even in best-in-class supply chains such as the high-tech ecosystem around ASML, achieving ecosystem optimization is a complex and labor-intensive goal. All partners must continuously invest in collaboration and communication.

Attempts to improve communication within and between large organizations through technology are nearly endless. ERP systems, S&OP processes, data sharing, and even blockchains once promised to solve everything. This is why the introduction of yet another solution should always be approached critically. And yet, we want to give it a try.

Generative and agentic AI have already shown what they can do in marketing. They automate decision-making and help organizations respond more quickly to customer needs. That same technology now presents a major opportunity in supply chains. By connecting processes more effectively and using information faster, AI agents can help build smarter, more efficient, and better-aligned supply chains. The potential is enormous, and the time has come to explore it.

AI agents are an extension of generative AI. They do not just use Large Language Models to generate text; they can also plan and perform tasks autonomously. AI agents can have different levels of autonomy. In supply chains, we mainly see potential in agentic workflows, where LLMs can perform tasks but only within predefined paths. Well-designed agentic workflows are controllable, do exactly what they are asked to do, are disciplined, and therefore highly predictable.

Since the introduction of agentic AI, automation has attracted renewed interest, and for good reasons. Time is a scarce resource in supply chains. Under time pressure, people can become careless, overlook details, and forget rules, also in communication. This is not criticism but just an observation. To automate communication effectively, we must be willing to let AI agents handle part of it.

Agents will need access to data from internal systems such as ERP, planning tools, and inventory information. They must understand the objectives and KPIs they are working with and know which data they may or may not share. With that foundation, they can communicate continuously with other agents or with people. The fact that communication becomes artificially intelligent makes data and information exchange far more effective than the flat data exchange that was long seen as the solution for chain collaboration. With the introduction of LLMs in the supply chain, we are taking an important step forward in the familiar field of the digital twin. We are teaching it to talk.