ShipmentIQ: Intelligent Shipment Process Automation

Where the pressure started.

High-volume shipment processing required teams to manually review Advance Shipping Notices (ASN/ASBN) and validate shipment information across multiple operational data sources. Identifying shipment errors and determining whether shipments should be accepted or rejected required significant manual effort. Limited visibility into historical patterns also made it difficult to proactively identify potential issues and consistently manage exceptions. The organization needed an intelligent solution that could learn from historical shipment data, provide predictive recommendations and integrate those insights directly into the operational workflow.

Designed for the real operating environment.

• Developed an ML-powered prediction engine that analyzes historical shipment and error patterns to recommend shipment acceptance or rejection.
• Integrated ML intelligence with Oracle APEX, giving users a centralized interface to review predictions, shipment details and exceptions.
• Automated shipment decision workflows, reducing repetitive manual validation and accelerating processing.
• Built an exception-management workflow to identify, investigate and resolve shipment-related errors.
• Integrated Oracle EBS data to bring together relevant shipment, supplier, item, organization and error information.
• Implemented feedback and writeback capabilities to capture user decisions, overrides and outcomes for continuous model improvement.
• Added reporting and monitoring capabilities to provide visibility into shipment decisions, exceptions and operational trends.