CIF Ownership Transfer Timing Using XAI Shipment Milestones
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Solution Overview
Problem
The complex issue of in-transit material ownership transfer for CIF shipments, where the seller's obligation to pay for insurance and freight continues after risk transfer to the buyer, necessitates optimization to align with actual transport delays, as fixed lead times can result in inefficient ownership transitions.
Innovation Solution
Capturing shipment milestone data using a permissioned distributed ledger and employing a machine learning model with explainable artificial intelligence (XAI) to predict optimal lead times for ownership transfer, providing transparent recommendations based on historical data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed lead times are used for ownership transfer in CIF shipments, then contract simplicity is maintained, but ownership transfer efficiency deteriorates due to misalignment with actual transport progress
Solution Approach 1:
The patent applies dynamics by transitioning from fixed lead times to dynamic, data-driven ownership transfer predictions. The machine learning model continuously learns from historical shipment data and adapts predictions based on real-time transport conditions, allowing the ownership transfer timing to dynamically align with actual transport progress rather than relying on static predetermined schedules
Solution Approach 2:
The system implements feedback by using historical shipment milestone data to train the machine learning model, which then generates predictions that are fed back into the contract optimization process. The model continuously improves by learning from past performance data, creating a closed-loop system where actual transport outcomes inform future ownership transfer predictions
2Reliability
If seller obligations continue after risk transfer in CIF shipments, then buyer protection is improved, but seller cost burden increases
Solution Approach 1:
The system applies preliminary action by using the machine learning model to predict optimal ownership transfer timing in advance. This allows parties to proactively plan and prepare for the ownership transfer event, optimizing when the seller's financial obligations end and the buyer's responsibilities begin, rather than reacting to unpredictable transport delays
Solution Approach 2:
The patent changes the parameter of ownership transfer timing from fixed to variable based on ML predictions. By adjusting the lead time parameter dynamically based on learned patterns from historical data, the system optimizes the balance between buyer protection and seller cost burden for each specific shipment scenario
3Loss of energy
If ownership transfer is delayed to avoid seller obligations, then seller cost is reduced, but transport coordination efficiency deteriorates
Solution Approach 1:
The machine learning model performs self-service by autonomously analyzing historical data and generating optimal ownership transfer predictions without requiring manual intervention. The system serves itself by continuously learning from past shipments and automatically adjusting predictions for future shipments, eliminating the need for manual contract negotiations based on experience
Data Source
AI summary
A method includes capturing shipment milestone data of cost, insurance, and freight (CIF) shipments, recording the shipment milestone data within a distributed ledger, generating a training dataset using past shipment milestone data recorded within the distributed ledger, and generating a machine learning (ML) model configured with explainable artificial intelligence (XAI) based on the training dataset. The method also includes receiving information regarding a CIF shipment from a computing device, determining one or more relevant features from the information regarding the CIF shipment, the relevant features influencing prediction of a lead time for ownership transfer, and generating, using the ML model configured with XAI, a prediction of a lead time for ownership transfer with an explanation of the prediction for the CIF shipment based on the determined relevant features. The method may also include sending the prediction of the lead time for ownership transfer with the explanation to the computing device.