Garbage In, AI Out: Why Data Discipline Drives Supply Chain Optimization

Broadcast Date: Thursday, April 16, 2026

Artificial intelligence is reshaping supply chain conversations, promising faster decisions, predictive insights, and automated optimization. But AI is only as effective as the data that fuels it. Without clean, structured, and governed data, even the most advanced AI models will amplify inaccuracies rather than eliminate them.

In this webinar, Elenna Dugundji, Director, Deep Knowledge Lab for Supply Chain and Logistics Research Scientist with MIT explores why data discipline remains the foundational driver of supply chain optimization. Our discussion will examine the critical role of data quality, governance, system integration, and process alignment in enabling meaningful AI outcomes. 

Attendees will gain practical insight into how leading organizations are strengthening their data foundations to unlock sustainable optimization—before, during, and beyond AI adoption.

Speaker: Elenna Dugundji, Director, Deep Knowledge Lab for Supply Chain and Logistics Research Scientist, MIT

Moderator: Brian Straight, Editor in Chief, Supply Chain Management Review

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