Transforming Grocery Inventory Planning and Decision Intelligence Using Data + AI on a Databricks Lakehouse Architecture

This paper presented Grocery Data Intelligence, a Data + AI-driven grocery inventory planning system built on a lakehouse architecture. The system addresses a practical retail problem: reducing stockouts and waste by converting raw grocery data into trusted metrics and recommended actions. The research demonstrates how data engineering and AI can work together to support faster business decisions. The Bronze layer stores raw data, the Silver layer cleans and standardizes data, the Gold layer prepares business metrics, and the semantic layer provides consistent definitions. The application then uses these trusted metrics to power dashboards, natural language questions, inventory storytelling, and action workflows. The project shows that the future of analytics is not limited to reporting. Data should help users understand what is happening, why it matters, and what action should be taken. Grocery Data Intelligence follows this direction by moving from dashboard visibility to decision support. The findings indicate that Data + AI solutions can help grocery stores plan better, reduce stockouts, reduce waste, and act faster. The approach can also be extended to other retail and supply chain domains where timely action is critical.

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