Solution Overview

In supply chain management, AI agents can tackle critical queries impacting service levels and revenue: Does our inventory and capacity meet current demand? Where might manufacturing delays arise, and how will they ripple through the chain? What workflow changes can reduce disruptions?

This cookbook details how to create a supply chain copilot leveraging the OpenAI Agent SDK and Databricks Managed MCP. MCP allows the agent to access both structured and unstructured enterprise data—like inventory records, sales figures, supplier updates, local events, and beyond—delivering real-time visibility, early shortage alerts, and forward-looking advice. At its core, an orchestration layer integrates:

• Queries on structured inventory, demand, and supplier data
• Time series forecasting for every wholesaler
• Graph-based raw material needs and transport optimizations
• Vector-indexed email archives for semantic searches in unstructured communications
• Revenue risk assessments

Upon completion, you'll have a deployed template that pulls from distributed data sources and predictive models, flags potential bottlenecks, and suggests corrective measures. It handles queries like:

• What products are dependent on L6HUK material?
• How much revenue is at risk if we can’t produce the forecasted amount of product autoclave_1?
• Which products have delays right now?
• Are there any delays with syringe_1?
• What raw materials are required for syringe_1?
• Are there any shortages with one of the following raw materials: O4GRQ, Q5U3A, OAIFB or 58RJD?
• What are the delays associated with wholesaler 9?

Stakeholders simply input natural-language prompts to get immediate responses. This guide provides step-by-step instructions to set up this solution in your environment.

Architecture

This cookbook's architecture stacks an OpenAI Agent atop your current Databricks analytics workloads. Databricks components become accessible via callable Unity Catalog functions. Built with the OpenAI Agent SDK, the agent links to Databricks Managed MCP servers.

The outcome is a unified, near-real-time chat interface offering precise forecasts, adaptive inventory guidance, and informed decisions throughout the supply chain. This setup creates an agent layer that taps into your enterprise data (both structured and unstructured), traditional ML models, and graph analytics features.