The AI-Driven Supply Chain: Advanced Training for Next-Gen Leaders

Discover how models work, from linear regression to ensemble models, with a focus on supply chain applications and predictive flow estimation.

An inside look at how MIT navigates AI ethics, data governance, and the transition of lab research into industry-standard SCM solutions.

A high-energy developer experience workshop tailored for supply chain professionals, focusing on the modern "vibes" of efficient coding and rapid prototyping.

Master the art of demand forecasting. Leverage advanced techniques to handle seasonality and volatility in global supply networks.

Go behind the scenes of modern AI to see how "neural networks" mimic the human brain to process complex data, learning how to train these models to recognize patterns and make high-stakes decisions.

Learn how to teach machines to "see" and interpret the visual world, using layers of digital filters to identify objects and automate visual inspections in supply chain environments.

Transition from predictive to prescriptive analytics. Explore Deep Q-Networks and policy gradients applied to multi-echelon inventory optimization and multi-period product pricing. 

Learn how natural language processing automates "the language of trade," using AI to accurately classify goods for international shipping and customs compliance.

Scale your expertise by exploring how the same transformer architectures manage massive, unstructured product catalogs and customer reviews at a global retail scale.

Train models and explore fine-tuning and retrieval-augmented generation to make LLMs "experts" in your specific domain. Understand safety mechanisms for production deployment of agents on limited hardware.

Explore state-space models as an alternative architecture for sequence modeling with long-range dependencies to predict maritime port congestion using AIS vessel tracking data.

Beyond chatbots. Build tool-enabled LLM agents that can autonomously browse data, execute code, and perform complex supply chain tasks. 

Architectural mastery. Deploy multi-agent systems and "chain-of-thought" workflows based on award-winning OpenAI competition entries. 

This is an intensive, one-week curriculum designed for leaders who need to bridge the gap between strategic AI oversight and high-impact supply chain implementation. Developed from material used in MIT’s elite Supply Chain Management Master’s program, the course provides a framework for integrating machine learning and deep learning into logistics networks.

The program is specifically designed for mid-career managers, leaders, and those overseeing the next frontier of global operations. It is ideal for individuals ready to become their organization’s "AI whisperer" or primary expert on AI adoption.

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Dates: Monday, July 20, 2026 – Friday, July 24, 2026.

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Location: The course takes place on-campus at MIT in Cambridge, MA.

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Format: It is a five-day intensive.


Tuition for the five-day intensive program is $6,000.

The course covers a broad range of advanced technologies, including:

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Machine Learning & Neural Networks: Fundamental models, from linear regression to deep learning.

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Generative AI & LLMs: End-to-end training of Large Language Models, fine-tuning, and retrieval-augmented generation.

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Predictive Analytics: Master time series forecasting for demand and lead time prediction.

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Advanced Robotics & Vision: Computer vision for automated inspections and reinforcement learning for inventory optimization.

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Autonomous Agents: Building tool-enabled LLM agents and multi-agent systems.


The curriculum is designed to be accessible to managers while providing value to experienced developers. A unique "vibe coding" workshop is included to help managers understand the developer experience and help coders harness new methods for rapid prototyping and efficiency.

Participants will leave with the ability to:

• Transition from reactive logistics to proactive, self-optimizing supply networks.

• Understand the logic, risks, and ROI potential of predictive analytics beyond the "black box" of AI.

• Leverage neural networks to compress lead times and eliminate operational bottlenecks.

• Implement AI governance and change management strategies within their organizations.


The course is led by prominent researchers and industry experts, including:

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Elenna Dugundji: Director of the Deep Knowledge Lab for Supply Chain and Logistics.

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Thomas Koch: Postdoctoral Associate.

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Kevin Power, Alex Carroll, Nikolay Aristov, and Ziyan Li: Research assistants and PhD candidates from MIT.


Yes. Each day includes a "Fireside Chat" with industry leaders who have successfully implemented AI solutions. These sessions focus on the real-world challenges of AI journeys on both the technological and human sides.

The final day of the course includes a site visit to a local company that is leading AI innovation, allowing participants to see these technologies in action. Throughout the week, students also engage in high-level, hands-on projects that simulate implementation challenges.