Logistics / Freight · n8n Workflow Automation
Logistics — Delivery Exception Management
Portfolio DemonstrationAn incoming (test-only) shipment-exception event is parsed and classified into one of six exception types, each producing a distinct mock action, before severity and an SLA countdown are calculated and high-value shipments get an extra escalation flag ahead of operations assignment.

Problem
Delivery exceptions like delays, damage, or failed attempts need fast triage to protect customer relationships and high-value shipments.
System Design
Exception fan-out: a Webhook trigger feeds a 6-way Switch (the widest fan-out in the portfolio), each branch with a distinct mock action, before reconverging into severity/SLA scoring.
Workflow Stages
- 016-Way Exception Routing
- 02Severity & High-Value Escalation
- 03Operations Response & Log
Tools Used
- n8n
- Webhook
- Code (JavaScript)
- If
- Set
- Switch
Safety & Human Review
- Workflow name begins with DEMO | and is inactive.
- No credentials are attached to any node.
- No enabled external send node exists anywhere in this workflow.
- All data is fictional (example.com emails, invented names/addresses).
- Uses only built-in n8n node types.
Implementation Evidence
Real screenshots of the working system — collapsed by default so this page stays readable.
What's Working
7 structural checks passed, including: Workflow JSON parses (local + server-returned copy); active is false; Name begins with DEMO |; No credentials attached to any node. Structural and logic validation completed. Live n8n execution pending manual safe test.
Limitations & Confidentiality
Classification and scoring logic is deterministic/rule-based rather than a live AI model, by design (provider-independent, fully inspectable, no credential required). No real system of record, messaging channel, or calendar is connected - every downstream action is simulated or drafted, never sent. Live n8n execution has not been triggered as part of this build.
Portfolio demonstration built for a fictional business with fictional data. It is not connected to any real client, system, or customer.
What I'd Improve Next
Replace deterministic classification with a real AI model behind the same confidence/human-review pattern where one exists. Connect the mock/simulated steps to real systems of record once reviewed and approved. Wire a real (approved) communication channel behind the draft-message steps.
Have a similar workflow to automate?
I'll tell you honestly whether automation is the right fix.