AI-Powered Supply Chain Data Insights Workflow

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This workflow automates supply chain analytics by integrating AI and BigQuery to provide instant insights on shipment data within a conversational interface. It begins with a chat trigger where users can request specific data analysis or reports related to shipments. An AI Control Tower Agent, configured with a specialized prompt, interprets user queries, sanitizes them, and formulates SQL commands targeting the ‘transport.shipments’ table in BigQuery.

The workflow then executes these SQL queries using a dedicated BigQuery node, retrieving data such as shipment delays, on-time performance, or delivery metrics. Results are returned to the user in a clear, structured format through the chat interface, which is powered by a language model. Sticky notes in the workflow provide setup guidance and explain key components.

This setup is ideal for logistics or supply chain managers who need quick, data-driven insights from their shipment databases through simple conversational requests. It streamlines data access, reduces manual querying, and enhances decision-making with AI assistance.

Node Count

11 – 20 Nodes

Nodes Used

@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.toolWorkflow, code, executeWorkflowTrigger, googleBigQuery, stickyNote

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