Sponsored by: Domo | Eliminating the Warehouse “Tail Problem”: AI-Powered Optimization on Databricks + Domo

Warehouse operations don’t fail at the start of the day—they fail at the end. As real-world conditions diverge from static planning assumptions, bottlenecks emerge across palletizer lanes, creating a costly “tail problem” that requires constant manual intervention. This session explores how one logistics operator reimagined warehouse automation by combining Databricks and Domo to create a self-adaptive system. As palletizers fall behind, go offline, or deviate from expected performance, the platform continuously analyzes live data from Databricks and recommends exactly what actions to take—what to move, where to move it, and why. Through an interactive Domo application, supervisors can execute AI-driven recommendations, simulate changes, and respond instantly to disruptions with automated evacuation workflows. The result: a self-aware, adaptive system that replaces reactive firefighting with proactive optimization—improving efficiency, reducing downtime, and delivering measurable ROI.