Automate replenishment with MMF, Databricks Genie, and Amazon Quick | Amazon Web Services
AWS Machine Learning Blog: technical walkthrough. Commands and expected outputs are from a working deployment. Substitute your own account values throughout.
Replenishment automation starts with a demand forecast, and in retail that forecast has a short shelf life. By the time a planner exports it, checks it against supplier availability, and works down tens of thousands of stock keeping units (SKUs), the fastest movers have stocked out. For years the hard part was the forecast itself. Foundation models changed that, predicting demand across an entire catalog with no per-item tuning. The bottleneck moved downstream to the gap between a good forecast and an order placed on it.
That gap persists because the forecast and the action live in different systems. The forecast sits in a governed data platform. Supplier availability lives in a separate operational feed. And the person who places the order has a login for neither. This post...
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