Case Study · McKinsey Operations · 5/5/2026

Retailer Automates Supply Chain Exception Handling, Cuts Stockouts by 34%

零售商自动化供应链异常处理,缺货率下降 34%

# operations⚡ automation⚡ data-analysis⚡ decision-supportGPT-4🔴 Dev needed
Why it matters
Supply chain exception handling is a giant game of telephone: system detects issue → analyst reviews → emails supplier → waits → follows up. Each handoff adds hours. An agent that detects and acts in the same loop collapses that chain entirely.

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

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.

Related Cases