Automated Fulfillment Exception Handling
Context
Client Stack + Scale
A consumer electronics seller using Shopify Plus with ShipBob as their 3PL partner. They process orders for tech accessories, cables, and small electronics across North America.
The Problem
What was breaking
Of the 600–900 daily orders, approximately 15% would get stuck in "exception" status in ShipBob – requiring manual intervention from the fulfillment team. Common exceptions included address validation failures (customer entered "Apt 3B" in address line 2 but ShipBob validation rejected it), inventory location mismatches (product physically in warehouse but not mapped to correct bin), and shipping method incompatibilities (customer selected overnight shipping but product dimensions exceeded carrier limits). The two-person ops team spent 4–5 hours per day triaging these exceptions in ShipBob's dashboard, researching context in Shopify, deciding on remediation (update address, reassign inventory location, contact customer), and manually executing the fix. During promotional periods, exception volume would spike to 250+ per day, causing fulfillment delays and overtime for the ops team.
15% of orders requiring manual fulfillment exception handling daily
Ops team spending 20–25 hours/week on repetitive exception triage
Average resolution time of 6–8 hours per exception (due to context switching)
No automated remediation for common, repeatable exception patterns
Customer complaints about delayed shipping during high-volume periods
The Solution
Architecture + Approach
We built an AI-powered operations agent that monitors ShipBob exceptions in real-time, classifies them by type, attempts automated remediation for known patterns, and escalates complex cases to humans with full context and suggested actions.
Architecture Overview
ShipBob webhook listener to capture exception events in real-time
Classification system using OpenAI GPT-4 to categorize exception types
Shopify Flow integration for automated remediation workflows
Python-based remediation engine with safe action framework
Escalation system with Slack notifications and suggested next steps
Technical Details
When ShipBob triggers an exception webhook, the system first fetches the full order context from Shopify (customer history, order notes, product details). The AI agent then classifies the exception into one of 12 known patterns (address validation, inventory location, shipping method, etc.). For known patterns with safe remediation paths, the system executes automated fixes: address validation exceptions are corrected using USPS address normalization API, inventory location mismatches trigger ShipBob location reassignment calls, and shipping method incompatibilities automatically downgrade to ground shipping with customer email notification via Shopify Flow. For exceptions that don't match known patterns or have ambiguous fixes, the system escalates to humans via Slack with a structured summary (exception type, order context, 2–3 suggested actions based on similar historical resolutions). All actions are logged with full audit trail, and humans can approve/reject suggested remediations before execution.
The Results
Measurable Impact
Down from 90–135 daily manual exception resolutions to 18–27, measured over 60-day post-launch window
For automated remediations, down from 6–8 hours when handled manually
Percentage of automated fixes that resolved the exception without human intervention or rollback
Ops team now spends 5–7 hours/week on exceptions instead of 20–25 hours
Additional Outcomes
Ops team reallocated saved time to supplier negotiation and SKU expansion planning
Customer complaints about shipping delays decreased by 65%
During promotional periods, exception handling no longer requires overtime
System learned 3 new exception patterns over first 90 days, further improving automation coverage
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