Shipment Milestone Imputation Using GPS and AIS Geofences
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Solution Overview
Problem
Carriers often provide incomplete and/or inaccurate itinerary and event data for shipments, leading to suboptimal supply chain performance, increased costs, and scheduling errors.
Innovation Solution
A supply chain management computing system that uses historical vehicle tracking data to perform clustering techniques to identify geofences for transportation locations, enabling accurate imputation of shipment milestones and predicting cargo arrival times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If carriers provide itinerary and event data for shipments, then shipment tracking information is available, but the data is often incomplete and inaccurate leading to suboptimal supply chain performance
Solution Approach 1:
The patent introduces an intermediary system that uses vehicle tracking data as a mediator to infer and impute shipment milestones. This intermediary layer processes raw tracking data through clustering algorithms to generate accurate milestone information, bridging the gap between incomplete carrier data and reliable shipment status information.
Solution Approach 2:
The patent replaces reliance on carrier-provided event data with an automated computational system that uses machine learning clustering algorithms to derive shipment milestones directly from vehicle tracking data. This substitution eliminates manual data entry and carrier reporting errors by using algorithmic inference from objective tracking information.
2Reliability
If complete and accurate shipment milestone data is obtained, then supply chain optimization is improved, but carriers often fail to provide timely and accurate event data
Solution Approach 1:
The patent performs preliminary clustering analysis on historical vehicle tracking data to pre-identify transportation locations and their geofences before shipment events occur. This preliminary action enables the system to automatically detect and impute milestones in real-time without relying on carrier-provided event data, ensuring complete and accurate tracking information.
3Measurement precision
If vehicle tracking data is analyzed using clustering techniques to identify transportation locations, then accurate geofences are created, but data processing complexity increases
Solution Approach 1:
The patent segments the vehicle tracking data processing into distinct stages: first clustering to identify stop locations, second clustering to identify transportation locations, and geofence creation. This segmentation breaks down the complex analysis into manageable steps, improving precision while making the system architecture more organized and maintainable.
Data Source
AI summary
The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world. Once these docking locations have been identified, a geofence can be established around those locations and used to determine when a shipping vehicle has arrived at the corresponding transportation location and/or docking location.