Freight Visibility Platform for Predictive Route Tracking Gaps
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Solution Overview
Problem
Supply chain visibility information, such as freight/load tracking data, is often unavailable, outdated, or erroneous due to factors beyond the provider's control, leading to inaccuracies and gaps in tracking and accuracy.
Innovation Solution
A predictive visibility system using AI and machine learning to predict freight/load routes and behaviors, even in constrained environments with limited or no real-time data, by analyzing load attributes, carrier information, historical data, and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data from sensors and IoT devices is used for tracking, then tracking accuracy is improved, but data availability becomes unreliable due to system failures, network issues, and environmental factors
Solution Approach 1:
The system performs preliminary actions by predicting future tracking data before it becomes unavailable. The machine learning model uses historical patterns and carrier behavior to forecast location and status information in advance, so when actual data becomes unavailable due to system failures or network issues, the predicted data can be used to maintain continuous tracking visibility.
Solution Approach 2:
The patent introduces an intermediary approach by using predicted data as a mediator between the unavailable real-time data and the tracking system. When actual sensor data or location updates are lost due to network connectivity issues or system failures, the predicted data fills in the gaps, acting as a mediator that maintains the continuity and reliability of tracking information.
2Reliability
If predictive visibility is provided without real-time data, then data availability reliability is improved, but tracking precision deteriorates due to predictions being based on historical patterns rather than actual location
Solution Approach 1:
The system implements feedback mechanisms where predicted visibility data is continuously compared with actual tracking data when it becomes available. This feedback loop allows the machine learning model to learn from discrepancies between predicted and actual values, progressively improving the precision of predictions. The feedback also enables the system to adjust its forecasting based on real-time carrier behavior patterns.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the level of prediction detail and accuracy based on the availability and quality of real-time data. When real-time data is available, the system switches to high-precision tracking mode. When data becomes unavailable, it automatically transitions to predictive mode with appropriate uncertainty indicators, effectively changing the operational parameters to match data availability conditions.
3Adaptability or versatility
If the system handles constrained environments with limited data, then adaptability is improved, but system complexity increases due to need for multiple data processing modes
Solution Approach 1:
The system achieves universality by designing a multi-functional data processing architecture where a single integrated platform can handle multiple scenarios: real-time data processing mode, predictive data generation mode, and hybrid mode combining both. The machine learning model serves multiple purposes including pattern recognition, anomaly detection, and future data forecasting, reducing the need for separate specialized systems for different data availability conditions.
Data Source
AI summary
Systems, methods, and non-transitory media are provided for dynamically predicting visibility of freights in a constrained environment. An example method can include determining attributes associated with a load transported by a carrier from a source to a destination, the attributes including an identity of the carrier, an identity of an industry associated with the load, an identity of a shipper of the load, load characteristics, and/or a pickup time of the load; based on the attributes, predicting a route the carrier will follow when transporting the load to the destination, at least a portion of the route being predicted without data indicating an actual presence of the carrier within the portion of the route, the data including location measurements from a device associated with the carrier and/or a location update from the carrier; and generating a tracking interface identifying the route the carrier is predicted to follow.