Trade Classification can become a huge bottleneck in your Trade Compliance management program if you still rely on hard rules based trade classification that becomes obsolete very quickly or use precious Trade Compliance Analyst workforce on paper-based, manual classification processes. ClassifAI™ leverages algorithmic intelligence to automate and standardize Trade Classification for a seamless and adaptive classification process.
ClassifAI™ is a machine learning-based Trade Classification Assist tool that learns from your existing classification data and predicts trade classifications for new products and serves them over the web to your Global Trade Management Application.
Predicts Trade Classifications including HTS, Schedule B, ECCNs, PGA data.
Harnesses the predictive power of product-related supply chain data already existing in your ERP.
Integrated with existing SAP GTS Classification tools/User Interfaces with over the web service to your Global Trade Management (GTM) application.
Predicted classifications are accompanied by predicted probabilities to drive system or user decisions.
Real-life use cases achieve 99% accuracy in predicting correct classification for fully qualified HS codes, and over 99% for 6 Digit harmonized classification.
Feature & Functions
Machine Learning Model Creation based on existing classification data.
- Product Description(s)
- Business Unit
- Classifications of other products supplied by the same vendor
- BOM hierarchy classifications – assemblies and finished products, etc.
- Classifications of the same product in other countries
Integration of Machine learning model into Trade Classification
- Classifications workbench
- Fully automated classification – classify based on top prediction
- Automated classification based on Threshold – classify if predicted probability > XX%
- Guided Classification – User chooses from predicted classifications
- Batch classification service – Performance intensive mass classification prediction of 100s products
- Audit Existing Classification decisions deviating from the mean
- Audit classification work performed by new classification analyst users
Periodic Model Re-training
- Re-training every week/month based on new and changed classifications
- Re-Training after update by authorities
ABAP Based GTS Adapter installed in the GTS application
- Adapter code in /ROPAAR/ Namespace
- Backward compatibility to GTS 10.1
- Badi implementations for customer-specific fields/logic
Ropaar cloud-hosted Machine Learning GPU HW/SW or Customer Data Center Edge Hardware
- Machine Learning Models exposed as REST APIs
- All communication over HTTPS
- Model APIs accessed via URL and API Keys
- ClassifAI GTS adapter builds Request Payload and Request headers with Key
Provide an additional measure of confidence for users when choosing obvious classifications.
Guide users to an answer when the probability level is high for a single AI proposed classification.
Flag a classification analyst to double-check when there is a conflict between assigned classification versus AI proposal.
Guide analysts to the top predicted possibilities when more than one possible classification is predicted.
Learn from other countries’ classifications without having to maintain mappings.
Audit new classification analysts.
Audit classifications deviating from the mean.