Generate AI inventory reorder recommendations from Airtable with GPT-4o, Slack and email

📘 Description This workflow automates end-to-end AI-driven inventory intelligence, transforming Airtable stock data into optimized reorder recommendations, daily operational summaries, and instant Slack alerts. It fetches all inventory rows, validates structure, computes reorder and safety-stock metrics using strict formulas, merges multi-batch AI output into a unified dataset, and distributes actionable insights across Email and Slack. Invalid or corrupted Airtable rows are logged to Google Sheets for audit and cleanup. The workflow ensures deterministic inventory math (zero improvisation), strict JSON compliance, and reliable multi-channel reporting for operations teams.

⚙️ What This Workflow Does (Step-by-Step)

▶️ Manual Trigger – Start Inventory Optimization Runs the full optimization and reporting pipeline on demand.

📦 Fetch Inventory Records from Airtable Retrieves all SKU records (ID, ItemName, SKU, quantities, reorder levels) from the Airtable Inventory table.

🔍 Validate Inventory Record Structure (IF) Ensures each record contains a valid id. Valid → routed to AI optimization Invalid → saved to Google Sheets.

📄 Log Invalid Inventory Rows to Google Sheet Captures malformed or incomplete Airtable items for audit checks and data hygiene.

🧠 Configure GPT-4o — Inventory Optimization Model Defines the AI model for stock-level calculations using strict formulas: SuggestedReorderPoint = ReorderLevel × 1.2 SuggestedSafetyStock = ReorderLevel × 0.5 StockStatus logic: Critical if QuantityInStock ≤ SuggestedSafetyStock Needs Reorder if QuantityInStock ≤ SuggestedReorderPoint OK otherwise

🤖 Generate Inventory Optimization Output (AI) The AI engine analyzes each SKU and returns: Suggested reorder point Suggested safety stock Updated stock status Clean structured JSON for each item All without markdown, hallucination, or additional logic.