Use AI to predict out-of-stock and keep shelves full!
Out-of-stock (OOS) is one of the biggest drivers of lost sales and poor customer experience in retail. This Improve On-Shelf Availability Solution Accelerator shows how to use real-time data, AI-driven OOS modeling and the Databricks Lakehouse Platform to detect and fix on-shelf gaps before they hurt revenue.
Get everything you need in a ready-to-run Databricks notebook, including pre-built code, sample data, and step-by-step instructions.
Key highlights
- Use real-time insights to react faster: Ingest streaming data to detect OOS situations in near real time and trigger rapid responses across stores and channels.
- Drive more sales with better availability: Apply AI-based out-of-stock modeling to keep products on shelves and reduce missed sales opportunities.
- Power both retail and supply chain use cases: Extend the accelerator from store-level on-shelf availability into broader supply chain optimization scenarios.
- Scale to any size operation: Build on the Lakehouse so your solution can grow from a single banner or region to global, enterprise-wide deployments.
Ready to start improving on-shelf availability with AI? Use this Accelerator to go from insight to impact and turn OOS into a competitive advantage.
