This website collects cookies to deliver better user experience. Cookie Policy
Accept
Sign In
The Wall Street Publication
  • Home
  • Trending
  • U.S
  • World
  • Politics
  • Business
    • Business
    • Economy
    • Real Estate
    • Markets
    • Personal Finance
  • Tech
  • Lifestyle
    • Lifestyle
    • Style
    • Arts
  • Health
  • Sports
  • Entertainment
Reading: AI-Driven Parsing for Logistics: Automating Freight Data Processing
Share
The Wall Street PublicationThe Wall Street Publication
Font ResizerAa
Search
  • Home
  • Trending
  • U.S
  • World
  • Politics
  • Business
    • Business
    • Economy
    • Real Estate
    • Markets
    • Personal Finance
  • Tech
  • Lifestyle
    • Lifestyle
    • Style
    • Arts
  • Health
  • Sports
  • Entertainment
Have an existing account? Sign In
Follow US
© 2024 The Wall Street Publication. All Rights Reserved.
The Wall Street Publication > Blog > Tech > AI-Driven Parsing for Logistics: Automating Freight Data Processing
TechTrending

AI-Driven Parsing for Logistics: Automating Freight Data Processing

Editorial Board Published February 22, 2025
Share
AI-Driven Parsing for Logistics: Automating Freight Data Processing
SHARE

Abstract

The logistics and transportation industry generates vast amounts of structured and unstructured data, requiring automated tools for efficient processing. Traditional parsing methods struggle  with scalability, content variability, and format inconsistencies across freight documents, shipment orders, invoices, and real-time tracking data. This article introduces a network-based content parsing system developed by Igor Fedyak, designed to receive, parse, and manage large-scale logistics and transportation data using configurable templates and distributed parsing devices. The system employs a management server to dynamically allocate parsing tasks, ensuring high-speed data extraction, improved accuracy, and seamless integration with freight management systems (FMS), transportation management systems (TMS), and enterprise resource planning (ERP) software. The proposed methodology enables automated freight data processing, dynamic route optimization, and real-time  load tracking, revolutionizing data management for logistics and transportation companies.

Contents
Abstract1.   Introduction2. System Architecture2.1  Overview2.2  Management Server2.3  Parsing Devices3.   Parsing Process and Workflow3.1  Content Reception and Filtering3.2  Parsing Assignment and Load Balancing3.3  AI-Driven Template Matching3.4  Integration with Logistics Software4.   Key Features and Advantages4.1  Real-Time Load Processing4.2  Automated Document Recognition4.3  Carrier and Route Optimization4.4  API Connectivity to TMS and ERP4.5  High Accuracy and Scalability5.   Experimental Results and Performance Evaluation6.   Future Directions7.   ConclusionReferencesAcknowledgments

1.   Introduction

The logistics and transportation sector relies heavily on real-time data processing for freight tracking, carrier management, load booking, and shipment processing. Manual data entry and traditional parsing techniques create inefficiencies, leading to delays, errors, and increased operational costs.

A major challenge in logistics is data fragmentation, where freight data arrives in diverse formats, such as:

  • Emails containing load requests
  • PDF invoices and shipment documents
  • Electronic Bill of Lading (eBOL) records
  • GPS-based real-time tracking feeds

This paper presents a scalable, networked parsing system developed by Igor Fedyak, specifically designed for logistics and transportation automation. The system leverages distributed parsing devices, AI-driven template matching, and real-time data synchronization, enabling logistics companies to process high volumes of freight data efficiently.

2. System Architecture

2.1  Overview

The proposed logistics-focused content parsing system consists of:

  • A management server that assigns parsing tasks and distributes workloads based on freight data volume.
  • A network of parsing devices that process load documents, extract shipment details, and format structured outputs.
  • AI-driven templates to recognize freight documents (eBOLs, invoices, load sheets, customs paperwork).
  • Real-time data synchronization with TMS, FMS, and ERP systems.

2.2  Management Server

The management server acts as the central control unit, handling:

  • Freight document ingestion from emails, TMS, or APIs.
  • Parsing assignment creation, distributing workloads based on device capacity.
  • Communication with parsing devices, ensuring efficient processing and real-time updates.

2.3  Parsing Devices

Each parsing device is responsible for:

  • Extracting structured freight data from unstructured sources (emails, scanned documents, XML files).
  • Applying AI-driven parsing rules to standardize load details.
  • Synchronizing data with dispatch systems, improving load matching and carrier selection.

3.   Parsing Process and Workflow

3.1  Content Reception and Filtering

  • Incoming freight data (eBOLs, invoices, load requests) is filtered and categorized by the management server.
  • Parsing rules and AI-driven templates extract key data fields, such as:
    • Load ID, pickup location, delivery location, carrier details
    • Freight weight, commodity type, special handling instructions
  • Estimated time of arrival (ETA), transit time, and route recommendations

3.2  Parsing Assignment and Load Balancing

  • The management server assigns parsing tasks to available devices based on:
    • Document complexity (e.g., structured vs. unstructured load requests)
    • Real-time freight volume
    • Carrier and shipper priority processing
  • Dynamic load balancing ensures:
    • Faster processing times for high-priority loads.
  • Efficient document parsing across multiple transportation hubs.

3.3  AI-Driven Template Matching

  • The system uses AI-trained templates to identify, classify, and process logistics documents.
  • Parsing devices recognize:
    • eBOL document layouts for different carrier
    • Customs documentation requirements
    • Invoice structures for financial reconciliation

3.4  Integration with Logistics Software

  • Parsed freight data is automatically synced with:
    • Transportation Management Systems (TMS) for real-time tracking.
    • Freight Marketplaces for automated carrier selection and rate optimization
    • Load Matching Platforms to identify available trucks.

4.   Key Features and Advantages

4.1  Real-Time Load Processing

  • The system processes freight requests in milliseconds, reducing manual entry delays.

4.2  Automated Document Recognition

  • AI-driven parsing extracts load details from emails, XML files, and scanned BOLs.

4.3  Carrier and Route Optimization

  • Parsed load data is used for automated dispatching, ensuring optimal carrier selection.

4.4  API Connectivity to TMS and ERP

  • The system integrates seamlessly with TMS, FMS, and financial software, automating invoicing and freight payments.

4.5  High Accuracy and Scalability

  • AI-based template learning improves parsing accuracy, reducing errors in freight invoices, BOLs, and customs forms.

5.   Experimental Results and Performance Evaluation

A performance evaluation was conducted using real-world logistics datasets, including eBOLs, invoices, and shipment records.

MetricTraditional ParsingProposed Parsing System
Parsing Speed (pages/sec)  20 pages/sec  150 pages/sec
Accuracy (%)  85%  98%
Integration with TMS  Limited  Full API Integration
Load Matching Speed  Slow  Real-time Matching

The proposed system processed freight data 7.5x faster than traditional methods.

  • Parsing accuracy improved by 13%, reducing errors in load assignments.
  • Automated carrier matching improved dispatch efficiency, reducing empty miles by 20%.

6.   Future Directions

Future improvements include:

  • AI-Powered Predictive Routing: Optimizing load scheduling based on real-time traffic and weather conditions.
  • Blockchain Integration: Secure document validation for customs and freight auditing.
  • Multilingual Document Parsing: Supporting global logistics operations with OCR-based translation.

7.   Conclusion

The logistics and transportation industry relies on real-time data processing for freight matching, load tracking, and route optimization. The network-based parsing system developed by Igor Fedyak introduces a highly scalable, AI-driven approach to automated freight document processing. By leveraging distributed parsing devices, real-time template matching, and API-based integrations, logistics companies can automate workflows, reduce errors, and improve operational efficiency. This system provides a transformative solution for freight carriers, shippers, and 3PL providers, paving the way for a fully automated logistics ecosystem.

References

  1. Fedyak, Igor. (2019). System and Method for Content Parsing (Patent No. 10911570).
  2. Additional peer-reviewed sources on logistics automation and AI-driven parsing.

Acknowledgments

This work is based on U.S. Patent No. 10911570, which presents an innovative approach to network-based content parsing in logistics and transportation. Special thanks to Igor Fedyak for contributions to the advancement of automated freight processing technologies.

https://www.linkedin.com/in/ifedyak

Share This Article
Twitter Email Copy Link Print
Previous Article Armed theft suspect shot, killed by Harmony liquor retailer worker Armed theft suspect shot, killed by Harmony liquor retailer worker
Next Article GOP praises Trump—after they aren’t sidestepping his insane habits GOP praises Trump—after they aren’t sidestepping his insane habits

Editor's Pick

California dwelling of lacking child’s mother and father searched; father has served time for youngster cruelty

California dwelling of lacking child’s mother and father searched; father has served time for youngster cruelty

San Bernardino County sheriff’s investigators on Sunday, Aug. 17, searched the house of the mother and father of the infant…

By Editorial Board 9 Min Read
Farmer Needs a Spouse: John Sansone and Claire Dinette Break up!
Farmer Needs a Spouse: John Sansone and Claire Dinette Break up!

Studying Time: 2 minutes It's sadly over for one more actuality tv…

4 Min Read
Steak ‘n Shake slams Cracker Barrel CEO for eliminating ‘old-timer’ from emblem: ‘We take delight in our historical past’
Steak ‘n Shake slams Cracker Barrel CEO for eliminating ‘old-timer’ from emblem: ‘We take delight in our historical past’

FOX Enterprise’ Jeff Flock experiences on Cracker Barrel unveiling a brand new…

4 Min Read

Oponion

Does Your Tongue Want a Exercise? A Holistic Skincare Professional Explains the Subsequent Massive Factor in Magnificence

Does Your Tongue Want a Exercise? A Holistic Skincare Professional Explains the Subsequent Massive Factor in Magnificence

Not too long ago, I’ve been eager about my face.…

October 3, 2024

Bear assaults and critically injures lady strolling her canine in Slovenia forest

A girl was in important situation…

May 28, 2025

Harriette Cole: My teen expects us to purchase her a brand new automobile

DEAR HARRIETTE: My 17-year-old daughter not…

July 25, 2025

Fox Information morphs into QVC when Melania Trump has merchandise to promote

After hyping the sit-down as Melania’s…

December 6, 2024

SF Giants’ Verlander inches nearer to returning, Adames set for time without work

SAN FRANCISCO — Justin Verlander took…

June 7, 2025

You Might Also Like

Gregory Hatanaka Teases His Biggest Films Yet with No Regrets and The Shout
EntertainmentTrending

Gregory Hatanaka Teases His Biggest Films Yet with No Regrets and The Shout

If you know indie cinema, you know Gregory Hatanaka. The veteran director, producer, and distributor has spent decades zigzagging through…

6 Min Read
Selecting the Proper MacBook Is not As Troublesome As You Would possibly Suppose
Tech

Selecting the Proper MacBook Is not As Troublesome As You Would possibly Suppose

The larger change is in what number of exterior shows the M3 MacBook Air can help. Whereas the M4 mannequin…

29 Min Read
Our Editors’ Favourite Large Display screen Chromebook Is Now 9
Tech

Our Editors’ Favourite Large Display screen Chromebook Is Now $159

Again to high school is at all times a good time to choose up a deal on a brand new…

2 Min Read
Okay So I Clicked on “Castle” at 2AM and Things Got Weird
Trending

Okay So I Clicked on “Castle” at 2AM and Things Got Weird

Princess. Dragon. Kingdom. Knight. Wait what? So I'm on this website called Embeddings.fyi at like 2 in the morning because…

6 Min Read
The Wall Street Publication

About Us

The Wall Street Publication, a distinguished part of the Enspirers News Group, stands as a beacon of excellence in journalism. Committed to delivering unfiltered global news, we pride ourselves on our trusted coverage of Politics, Business, Technology, and more.

Company

  • About Us
  • Newsroom Policies & Standards
  • Diversity & Inclusion
  • Careers
  • Media & Community Relations
  • WP Creative Group
  • Accessibility Statement

Contact

  • Contact Us
  • Contact Customer Care
  • Advertise
  • Licensing & Syndication
  • Request a Correction
  • Contact the Newsroom
  • Send a News Tip
  • Report a Vulnerability

Term of Use

  • Digital Products Terms of Sale
  • Terms of Service
  • Privacy Policy
  • Cookie Settings
  • Submissions & Discussion Policy
  • RSS Terms of Service
  • Ad Choices

© 2024 The Wall Street Publication. All Rights Reserved.

Welcome Back!

Sign in to your account

Lost your password?