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Can GenAI agents ๐Ÿค– manage a supply chain? Lessons from the classical Beer Game.

  • 1.  Can GenAI agents ๐Ÿค– manage a supply chain? Lessons from the classical Beer Game.

    Posted 08-29-2025 11:00

    Hi everyone,

    A joint team from #Harvard Information Theory Lab, #MIT Data Science Lab, and #GeorgiaTech Business School has built the first live simulation of the Beer Game powered by LLMs ๐ŸŽฏ

    Experts suggest that fully autonomous supply chains-where AI makes all the supply chain (e.g., inventory) decisions-may be just around the corner. But how close are we really? To explore this, we reimagined the classical Beer Game: every facility is now run entirely by a GenAI agent.

    ๐Ÿ”น In decentralized mode, each agent makes independent ordering decisions.

    ๐Ÿ”น In centralized setups, a supply chain orchestrator provides the different agents with varying levels of information sharing. Decisions are still made only by the individual agents.

    ๐Ÿ”น Performance is evaluated through the total supply chain cost and the bullwhip effect, which are key indicators of supply chain efficiency.

    The simulation supports multiple state-of-the-art Large Language Models (#GPT, #Llama, #DeepSeek, #Phi) and offers tunable parameters, letting you experiment with diverse supply chain scenarios.

    ๐Ÿ‘‰ Try the interactive simulation here: https://infotheorylab.github.io/beer-game/  

    At the end of the page, you will find "Lessons Learnt from the GenAI Beer Game" but of course, you are welcome to draw your own conclusions.

    Best

    David



    ------------------------------
    David Simchi-Levi
    Professor of Engineering Systems
    MIT
    Cambridge MA
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  • 2.  RE: Can GenAI agents ๐Ÿค– manage a supply chain? Lessons from the classical Beer Game.

    Posted 08-30-2025 08:50

    David,

    Thanks for sharing! For anyone interested in how you can use LLMs to help build simulations and test different things quickly, this is a fantastic example.

    Besides the obligatory WarGames movie reference, "Shall we play a game?" that immediately came to mind, just in the few minutes that I played around with this simulation, it's not only a great way to test the utility of current AI models as agents/decision-makers, but simulations like this can more easily implement tests of policies (human versions of 'prompts') that you might give an employee. And the fact that GPT-5-mini does better shows that the AI researchers are still making progress on reasoning and problem-solving capability, though running it on gpt-5-mini does take significantly longer in your simulation app (gpt-4o-mini: ~15 sec; gpt-41-mini: ~20 sec; gpt-5-mini: ~5 min).

    Even running a single instance with each of the three OpenAI mini models (this sim has info sharing & downstream inventory visibility 'on') gives us questions to think about, one of which is what should be our trade-off between time/cost of the decision vs the improvement? In these runs, I saw improvement from gpt-4o-mini to gpt-41-mini, but not as much from gpt-41-mini to gpt-5-mini.

    gpt-4o-mini: 

    gpt-41-mini:

    gpt-5-mini:



    ------------------------------
    Warren Hearnes, PhD
    Founder, OptiML AI
    INFORMS Board Role: VP Technology Strategy
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    Original Message


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