Machine-Learning Capacity Forecasting for Shipping Networks
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Solution Overview
Problem
Conventional route planning engines fail to communicate capacity changes effectively among transportation entities, leading to delays, loss of items, or underutilized capacity due to unexpected events like weather conditions, and lack of historical learning and prediction capabilities.
Innovation Solution
A capacity recommendation feature using machine learning algorithms analyzes capacity changes, historical data, and uncontrollable event data to generate recommendations for modifying shipping network components, ensuring timely communication and optimization across entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional route planning engines are used to generate routes and predict capacity limitations, then basic routing functionality is provided, but capacity changes are not communicated to other transportation entities and historical learning capabilities are lacking
Solution Approach 1:
The patent implements a feedback mechanism where capacity changes detected by one transportation entity are communicated to other entities in the network. The system continuously monitors capacity status and propagates updates throughout the network, ensuring all entities have current information about capacity limitations and changes.
Solution Approach 2:
The patent introduces a centralized capacity management system that acts as an intermediary between transportation entities. This mediator collects capacity data from various entities, processes it using machine learning algorithms, and distributes updated capacity information to relevant parties, eliminating the need for direct peer-to-peer communication infrastructure.
2Reliability
If conventional route planning engines operate independently without learning capabilities, then implementation is simple, but they fail to predict capacity changes due to expected events
Solution Approach 1:
The patent implements preliminary action by using machine learning algorithms to predict future capacity changes before they occur. The system analyzes historical data and uncontrollable event patterns to forecast capacity limitations in advance, allowing transportation entities to proactively adjust their routes and plans.
Solution Approach 2:
The system employs self-service through autonomous machine learning models that continuously learn from historical capacity data and uncontrollable event information. The algorithms automatically update their predictions and improve accuracy over time without requiring manual intervention or reconfiguration.
3Productivity
If transportation entities do not share capacity information, then system operation is straightforward, but delays and loss of items occur due to unexpected events
Solution Approach 1:
The patent creates a universal capacity information platform that serves multiple transportation entities simultaneously. The system collects, processes, and distributes capacity data to various carriers and logistics providers, enabling them all to benefit from shared intelligence without requiring individual entities to build their own prediction capabilities.
Solution Approach 2:
A centralized intermediary system aggregates capacity information from multiple transportation entities and distributes it network-wide. This mediator ensures that all participants receive relevant capacity updates, enabling coordinated response to unexpected events and improving overall network productivity.
Data Source
AI summary
Techniques for generating a capacity recommendation engine are described herein. First information that identifies capacity changes associated with shipping packages from one or more entities may be obtained. Each capacity change may be associated with a reason code that identifies a reason for the capacity change. Second information that identifies uncontrollable event data for one or more geographic areas that correspond to a delivery area for the one or more entities may be received. Historical information that includes historical capacity changes and associated reason codes from the one or more entities may be obtained. A machine learning algorithm may be implemented based on the first information, the second information, and the historical information. A recommendation may be generated for an entity using the machine learning algorithm. The recommendation may include a modification of a component associated with the shipping network of the entity for a future time period.