Analytics · Customs Brokers
ACE filing error and rejection analytics
A broker analyzed ACE rejections to find the recurring causes and cut the rework that slowed clearance.
Microsoft FabricPower BIAzure Data FactoryDAX measures
- Rejections
- -34%
- illustrative
- Causes
- Categorized
- and ranked
- Rework
- Reduced
- at clearance
- Monitoring
- Continuous
- with trend
Illustrative scenario · example figures
The challenge
ACE rejections were handled one at a time with no view of the patterns, so the same avoidable errors kept returning and quietly added days to clearance.
What we built
We categorized rejection messages and tied them to filer, client, and entry type, surfacing the handful of causes behind most of the rework.
- Rejection categorization by message, filer, and client.
- Pareto of the causes behind most rejections.
- A trend view that confirms whether fixes are working.
Outcomes
- The few causes behind most rejections are named and fixed.
- Rework and the delay it adds to clearance both fall.
- Filing quality is monitored instead of assumed.
Where the data came from
ACE / ABIBroker filing systemRejection logsEntry master
Data is drawn from the systems the operation already runs on and delivered in the client’s own tenant.
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