What is Exception Handling in Logistics?
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Quick Answer
Exception Handling in logistics is the process of identifying, prioritizing, and resolving deviations from planned shipment workflows—such as delays, damages, route changes, or documentation issues. AI-powered exception handling automates detection, root cause analysis, and resolution recommendations to minimize disruption and cost.
Definition
Key Points
- Detects exceptions in real-time from tracking and sensor data
- Classifies exceptions by type, severity, and root cause
- Prioritizes high-impact exceptions for immediate attention
- Recommends resolution strategies based on historical outcomes
- Automates carrier communication and customer notifications
- Reduces exception resolution time by 40-50%
How It Works
Exception Detection
AI monitors tracking data and identifies deviations from plan
Classification
Exception is categorized by type (delay, damage, route issue, POD mismatch)
Impact Assessment
AI evaluates severity, customer impact, and time sensitivity
Resolution Recommendation
System recommends actions based on similar past exceptions
Execution & Learning
Resolution is tracked and outcome feeds continuous improvement
Frequently Asked Questions
What types of logistics exceptions can AI handle?
Delivery delays, shipment damages, POD (proof of delivery) mismatches, incorrect routing, carrier capacity issues, detention and demurrage, customs holds, and documentation errors.
How does AI prioritize exceptions?
AI considers customer tier, shipment value, delivery urgency (just-in-time orders), cascade impact on downstream shipments, and historical customer sensitivity to delays.
Can exception handling integrate with TMS systems?
Yes. AI exception handling connects to major Transportation Management Systems (TMS) via API, receiving real-time data and pushing resolution recommendations back into operational workflows.