Will AI replace a Material Requirements Planner?
AI risk 78/100Opportunity 85/100Future demand 65/100
How AI is affecting this role
- ›A Python script connected to an email API reads unstructured shipping updates from vendors, extracts the new ETA, and automatically updates the delivery date in SAP.
- ›Excel Copilot analyzes five years of historical consumption data to suggest a dynamic safety stock level that accounts for India's monsoon-driven logistics delays.
- ›An AI agent scans supplier emails for keywords like 'force majeure' or 'shortage' and automatically flags high-risk items to the production manager before the shop floor stops.
Ways to survive
- ›Move away from manual data entry; learn to use Power BI to visualize inventory health instead of maintaining static spreadsheets.
- ›Focus on 'Supplier Relationship Management'—AI cannot drink chai with a vendor to get your parts prioritized during a crunch.
- ›Master the 'exception' handling: AI handles the 95% of orders that go smoothly; you own the 5% that threaten production.
Ways to get ahead with AI
- ›Build a custom n8n workflow that monitors your ERP for stock dropping below the Reorder Point (ROP) and drafts the PO for your one-click approval.
- ›Use Claude 3 to write complex SQL queries for your ERP to analyze 'Order Variance' (Ordered vs. Delivered) across top suppliers.
- ›Train a simple classification model to categorize supplier delay reasons (Logistics vs. Raw Material vs. Labor) to identify systemic supplier weaknesses.
How ONROL helps
Learn to build automated workflows using n8n and Python to connect legacy ERPs (like SAP or Oracle) with modern AI tools, enabling real-time inventory tracking and automated procurement triggers.
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