LTL Shipment Matching to Cut Deadhead Miles and Wait Time
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Solution Overview
Problem
The traditional freight industry faces inefficiencies due to unpredictable truck availability, leading to 'deadhead' miles, waiting times, and increased costs, as well as challenges in just-in-time inventory management and shipping predictability.
Innovation Solution
A computer-implemented method and system that receives and processes data for less-than-truckload shipments, identifying suitable trucks and assigning shipments based on availability, with the ability to control autonomous driving to optimize routes and reduce inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional broker methods are used to contact carriers, then shipment requests can be processed, but truck availability is unpredictable leading to deadhead miles and waiting times
Solution Approach 1:
The system performs preliminary actions by proactively searching for and securing return shipments for trucks before they complete their current deliveries. The automated broker system identifies potential return loads and reserves truck capacity in advance, ensuring trucks have confirmed return assignments before finishing their current routes. This eliminates deadhead miles by pre-arranging return cargo and reduces waiting times by having shipments ready to assign immediately upon truck availability.
Solution Approach 2:
The system implements continuous feedback loops where truck location, delivery status, and availability data are automatically tracked and fed back into the shipment matching algorithm. The automated broker receives real-time updates on truck completion status and immediately searches for return shipments based on this feedback. This creates a dynamic system that adapts to changing truck availability, maintaining high predictability and minimizing idle time through responsive reassignment.
2Productivity
If manual broker coordination is used, then carriers can be contacted, but the process is inefficient and increases shipping costs
Solution Approach 1:
The system enables self-service automation where the broker system operates autonomously without human intervention. The automated broker continuously monitors truck availability, searches the shipment database for matching return loads, evaluates compatibility criteria, and assigns return shipments automatically. This eliminates manual coordination labor and accelerates the matching process, dramatically improving productivity while reducing operational costs through automated decision-making.
Solution Approach 2:
The system changes the operational parameters of broker coordination from manual to automated processing. By transforming the broker function into an automated algorithmic system, the processing speed increases and cost per transaction decreases. The automated system can evaluate multiple return shipment options simultaneously and make optimal assignments based on predefined criteria, improving efficiency and reducing overall shipping costs through scalable automated operations.
3Reliability
If trucks are assigned without advance planning, then flexibility is maintained, but delivery time predictability decreases
Solution Approach 1:
The system performs preliminary assignment actions by securing return shipments for trucks before they complete their current deliveries. The automated broker proactively identifies compatible return loads and reserves truck capacity in advance, creating binding return assignments that guarantee truck availability for future shipments. This advance planning ensures predictable delivery times by having confirmed cargo ready to assign immediately when trucks become available, eliminating uncertainty in the coordination process.
4Productivity
If more trucks are kept available, then shipment requests can be fulfilled, but deadhead miles and empty return trips increase
Solution Approach 1:
The system performs preliminary actions by proactively securing return shipments for trucks before they complete their current deliveries. The automated broker continuously searches for and reserves return cargo assignments in advance, ensuring that when trucks finish their current routes, they immediately have confirmed return loads. This eliminates deadhead miles by pre-arranging return cargo, maximizing truck utilization without requiring excess idle trucks to maintain shipment fulfillment capability.
Data Source
AI summary
A shipment system can coordinate a less-than-truckload shipment with one or more freight vehicles by receiving from a first computing device, data corresponding to a new less-than-truckload shipment request. The data can comprise a pickup location, a delivery location, a pickup time, and a delivery time. Additionally, the shipment system can identify, from accessing a database, one or more trucks that are assigned to a current less-than-truckload shipment and are capable of executing the new less-than truckload shipment while completing the respective current-less-than-truckload shipment. A message corresponding to an invitation for executing the new less-than-truckload shipment can be transmitted to one or more operator computing devices of the identified one or more trucks capable.