🏭 Supply chain planners spend hours in Excel looking for the root cause of delays. Can an AI agent replace that work?
In logistics, when only 73% of shipments arrive on time, someone has to investigate why. The problem: the warehouse blames transport, transport blames the airport, and the planner ends up mediating in Excel for hours.
🤖 The proposal: an AI agent with access to the tracking database
The author connected Claude Opus to a shipment tracking database via MCP (Model Context Protocol) and asked it real questions:
“What is the responsibility of the warehouse team in overall performance?”
The agent analyzes timestamps and boolean flags to identify at which step in the chain the delay was generated:
- Did the order arrive late to the WMS? (+24h lead time)
- Did the warehouse finish picking after the loading cutoff? (+24h)
- Did the truck arrive at the airport after the cutoff? (+24h)
- Did the flight land outside customs hours? (+24h)
💡 Result: The agent can identify who failed at which step, arbitrate disputes between teams, and generate insights without hallucinations — because it’s connected to real data.
💡 Explanation in a nutshell#
Supply chain delays are cumulative: if the warehouse is 2 hours late, the truck misses the cutoff and the plane leaves without the shipment. Connecting an LLM to a database with real timestamps enables root cause analysis in seconds instead of hours. The key is that the model reasons over structured data, not just generates unsupported text.
More information at the link 👇
