Reimagining Supply Chain with Big Data and AI: The "analog" supply chain is dead. In its place is a living, breathing Digital Thread—a continuous flow of data that turns uncertainty into foresight. This course isn't just about software; it’s about the fundamental reimagining of how goods, information, and value move across the globe. We’ll move beyond spreadsheets to explore the autonomous, self-healing supply chains of the future.

New tools:

We move past basic automation to explore the "Brain" of the modern enterprise. We’ll analyze how big data becomes actionable intelligence through:
•The Digital Twin: Creating a real-time virtual mirror of your physical assets to test disruptions before they happen.
•Predictive vs. Prescriptive Analytics: Moving from "What happened?" to "What should we do right now?"
•Edge Computing & IoT: Harvesting data from the very edges of your network—from smart containers to warehouse robotics.

The Competitive Frontier:

•Harnessing Big Data and AI provides a "Decision Advantage" that legacy competitors cannot match:
•Hyper-Personalized Forecasting: Slashing inventory waste by predicting consumer demand at the local level.
•Autonomous Resilience: AI systems that automatically reroute shipments in response to geopolitical or climate shocks.
•Transparent Integrity: Using AI to audit massive datasets for ethical violations and ESG compliance across thousands of suppliers.
•Asset Velocity: Optimizing every square inch of warehouse space and every liter of fuel through machine learning.

This class is your gateway in equipping you with the technical literacy and strategic vision to:

•Design a data-driven supply chain strategy that integrates AI at every node.
•Evaluate the trade-offs between different AI models (Generative vs. Predictive) for logistics.
•Lead cross-functional teams in breaking down data silos.
•Navigate the ethics of AI, from algorithmic bias to the future of the human workforce.

Class attendance is compulsory

Course will involve reading and active discussion on topics assigned to you each day. Final case analysis has to be sketched out and submitted later.

•Exercise: The "Supply Chain Oracle" Simulation. In this hands-on lab, we will use Large Language Models (LLMs) to act as a Prescriptive Analytics Engine during a simulated global crisis.
•The Objective: Use an LLM to synthesize fragmented data (news feeds, weather reports, and supplier inventory logs) into a rapid-response mitigation plan.

The Workflow:
•The Prompt Engineering Phase: You will be provided with a "Data Mess"—unstructured text from various sources. You will learn to draft a "System Prompt" that instructs the AI to act as a Supply Chain Data Scientist.
•Multimodal Synthesis: Use the AI to cross-reference satellite imagery data (provided in text descriptions) with inventory levels to identify which "Tier 3" suppliers are currently underwater or offline.
•The "Strategy Stress Test": Ask the AI to simulate three different recovery strategies—ranking them by cost, speed, and carbon impact—then defend the "optimal" choice to a simulated Board of Directors.
•Pro Tip: We will focus on "Human-in-the-Loop" AI—learning how to spot when an algorithm is hallucinating a supply route that doesn't exist and how to course-correct the machine.

Grading: TBA (Case analysis, presentation (group), written assignment (group)

SCM grading scale:

Percentage    Points         Grade
90 - 100        22.5 – 25     Excellent (1)
80 - <90       20 - <22.5    Good (2)
70 - <80       17.5 - <20    Satisfactory (3)
60 - <70       15 - <17.5    Passed (4)
< 60              <15             Failed (5)

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Cheating and Plagiarism:

Cheating in any form and plagiarism will not be tolerated and result in severe penalties. Plagiarism is the use of words and ideas of others without attribution or without quotation marks or accompanying footnotes. In the extreme, plagiarism may result in failure of the course.