零售商自动化供应链异常处理，缺货率下降 34%

The Problem

A multi-brand retailer managing 80,000 SKUs across 12 distribution centers was generating 200+ supply chain exceptions per week: late shipments, inventory discrepancies, demand spikes, supplier failures. A team of 6 analysts was manually triaging these, emailing suppliers, and updating the ERP system. Average resolution time was 3.2 days.

The Agent Solution

They built an exception handling agent that runs every 4 hours. The agent:

1. Queries the ERP for inventory anomalies and pending exceptions
2. Classifies each exception by type and urgency using historical resolution patterns
3. For routine exceptions (late shipment, minor inventory discrepancy): automatically contacts the supplier via email template, logs the action in the ERP, and sets a follow-up trigger
4. For complex exceptions: prepares a briefing document and routes to the appropriate analyst with recommended actions

Results

• Exceptions handled automatically: 94% (200 → 12 per week requiring human action)
• Average resolution time: 3.2 days → 6 hours
• Stockout incidents: -34%
• Analyst headcount for exception handling: 6 → 2 (4 redeployed to strategic sourcing)
• Supplier response rate to agent emails: 87% (same as human-sent emails)

The Classification Model

The routing decision was the hardest part. They trained a classification model on 18 months of historical exceptions with outcomes — which ones needed human intervention and why. The model now routes with 89% accuracy. When it's uncertain, it routes to human review with a confidence score.

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