It’s Time to Automate Your Operations with AI-Based Solutions and Document Processing Automation for Logistics Data

Dorota Owczarek - September 17, 2021

Automated document processing is on the rise. Whether it’s document scanning or data extraction, document-driven tasks are being automated at a rapid pace. The logistics industry is no different. One of the most promising examples of this trend in logistics is AI document automation powered by machine learning. This new technology can be applied to improve customer service, track bills of lading, gather CMRs, analyze invoices, process data faster and more accurately, and more!

The solution provides many benefits that are crucial for today’s digitalized world of supply chain management. This blog post will highlight how document processing automation works and how it can benefit the logistics industry through increased productivity and reduced cost of doing business.

What is document processing automation?

How AI can extract data from any document with intelligent document processing

How AI can extract data from any document with intelligent document processing

Document processing automation or intelligent document processing (IDP) uses document scanning and data extraction processes to automate document-driven tasks.

The technology can be applied in a wide range of industries, including banking, insurance, energy management, healthcare, etc. Still, it’s beneficial for logistics companies that need to process large volumes (and varieties) of documents daily. Using document automation powered by machine learning allows these businesses to extract relevant information from any type or source of the document - whether it’s invoices and bills of lading or order information and customer feedback surveys. This enables deeper insights into the business situation and improved decision making across all areas: marketing & sales; risk management; finance; production planning; supply chain operations etc.

Intelligent document processing frees document-driven tasks from time-consuming and costly manual document processing, thus increasing the productivity of logistics teams.

Challenges of document processing for transporters

Document processing and data extraction can be a cost-intensive, time-consuming, and error-prone process. The quality of the results is often dependent on how well-trained people are to extract accurate information from documents with complex layouts or formats that may contain fuzzy text, blurry images, etc.

Although technological advancements in RPA (robotic process automation) document processing technology have improved the efficiency of this process for many industries, certain challenges have prevented transportation businesses from obtaining the same advantages.

Documents in the transportation sector don’t follow any established formats. Bills of lading, CMRs, proofs of delivery, purchase orders, invoices, and other documents are examples of this. While most of them include comparable elements such as consignee information and package volume and dimensions, each carrier will utilize its own format, with significant variability.

Document scanning should allow you to capture all information from any source without impacting document layout or image quality. The use of OCR alone introduces numerous issues as it cannot account for layouts changing across multiple pages within the same document or between two images with exactly identical dimensions but slightly different content due to drapery patterns etc., which leads to false interpretations by automatic extraction systems and ultimately incorrect decisions made using those results downstream.

Not every document is prepared in the same language. It’s a regular challenge for IDP to detect the language of a document automatically. This is a particular issue in transportation, where documents can be issued in different languages and contain data that require translation.

How AI enables smart automation for logistics document processing?

A document can contain a huge amount of information to be processed, organized, and analyzed. Artificial intelligence-powered document processing automation uses machine learning algorithms to automatically run several separate tasks like detecting the language in which a document is written, extracting relevant data from different types of documents with no loss in quality or accuracy, identifying document elements within an image. Hence, they are easier for humans to understand when analyzing them later on.

Human experts train machine learning technology to use statistical pattern recognition methods such as artificial neural networks and decision trees using labeled training examples provided by domain experts. This helps the machine learning model learn how specific patterns look and generalize their knowledge into new situations where previously unseen patterns need to be detected. AI for document analysis heavily relies on natural language processing (NLP) tasks. NLP is a field of computer science, artificial intelligence, and computational linguistics related to the interactions between computers and human (natural) languages. To understand the technicalities better, you can head off to Our Definitive Guide to Natural Language Processing.

How is IDP different from OCR?

In document processing, optical character recognition (OCR) is a mature technology that has been widely used for decades. The task of OCR is to convert an image of text into readable text. It is the first step to follow up with further document-related automation. Usually, OCR systems are coupled with rule-based extraction methods to analyze the document further. The technology works well with structured or semi-structured data. Although it remains the most popular document recognition and analysis option due to its accuracy, this method also comes with limitations such as low flexibility, making it hard for companies to change how they work based on new requirements or business processes.

OCR vs AI based IDP

OCR vs AI based IDP

A machine learning algorithm trained towards intelligent document processing can automatically detect different document elements without human guidance by leveraging computer vision techniques combined with natural language processing technologies. Hence, there’s no need to manually define document layout and content extraction rules if they change over time. Extracted data can be further analyzed and processed to automate various business use cases and improve how logistics data is managed. The AI-based solution can extract key information from digital documents in real-time, for example, invoice items and totals. It looks at document images as a whole instead of character by character, so even if there are lots of typos or something illegible in an item description, it will still figure out what’s being sold based on the product codes associated with each item. Machine learning solutions combined with standard software development can create powerful automated workflows that accommodate business processes and aid a logistics company’s digital transformation.

How to overcome document processing and analysis challenges at scale with machine learning?

The document automation powered by machine learning is still in its infancy compared to other AI use cases - like image identification or natural language processing (NLP). However, it’s growing fast, with many companies already embracing this technology. Document scanning and data extraction are good entry points into the world of artificial intelligence because they come with several advantages that make them especially attractive to businesses across the logistics industry.

Custom machine learning solutions for logistics document processing

Custom machine learning solutions for document processing let you process logistics documents in three steps to cover full workflows and beyond. (1.) Read the file to extract all the necessary data in a structured way. (2.) Refine the data with AI-based data enrichment and postprocessing. (3.) Apply normalized data to workflows and applications

While some components of intelligent document processing automation may be available off-the-shelf as cloud solutions, only machine learning algorithms trained specifically for your business deliver accurate results that will help you save money while improving operational efficiency through a more customized approach.

Benefits of introducing custom AI solutions for document processing

Benefits of custom AI solution for document processing

Benefits of custom AI solution for document processing

A document processing automation solution powered by artificial intelligence enables logistics companies to process data from documents with ease and speed while also increasing the accuracy of document analysis. This technology will enable you to reduce costs and increase productivity across your company’s document-driven processes.

Automation

One of the greatest benefits of intelligent document processing is documenting in a faster and more convenient manner. IDP powered by artificial intelligence will assist with tasks such as document analysis, process document data for a customer in a customer services department, or analyze inbound purchases from an invoice. The possibilities of additional automation are almost endless. Once you have extracted, normalized data you can use it to improve your workflows further. Since document data is normalized and structured as per the document type, it provides all required information in one place without any manual input.

Accuracy

ML may be used to help you process paperwork more quickly and accurately, resulting in fewer mistakes caused by manual entry. Automating document analysis eliminates all human errors associated with document data parsing, annotation of documents, etc. IDP powered by AI will identify patterns in document data to ensure the accuracy of results, which can further enhance your business processes. Using document automation technology, you have assured a consistently high-quality result with every document processed through this system.

Efficiency

IDP document processing software saves time since it is an automated system that reduces the need for manual input. This will reduce errors and increase productivity, allowing your employees to focus on other tasks that are more important than data extraction from documents.

Money Savings

Having time and money is a must in the logistics industry, which heavily relies on document-related tasks. However, not every company has a dedicated document analyst to handle operations 24/7 or enough human resources to process thousands of documents. This can quickly increase costs as well as risk for errors due to simple mistakes. IDP saves costs due to faster processing, increased accuracy, and reduced staffing requirements.

Data output is just the first step to drive workflows automation

For document scanning and data extraction from digital and paper documents, a custom machine learning solution is the best way to overcome challenges at scale. Here’s why:

Allocating transportation resources more effectively than ever before

Predict customer demand in real-time; plan ahead transportation resources for expected future needs; identify potential bottlenecks earlier so that they can be resolved before their impact on business performance.

Providing a 360 view of every delivery

Monitor the status of all shipments simultaneously at any point throughout its journey, ensuring complete visibility into where it is located along with live updates on who has possession.

Digitizing paper documents

Progress document scanning and data extraction with intelligent document processing automation powered by machine learning. This will make document storage obsolete, freeing up space for other important company files while speeding up search times dramatically.

Ease of creating reports

Create reports automatically by pulling information from several sources and utilizing document templates to create in-depth reports. Normalized data can be easily generated into a report’s structure that can further analyze business processes.

Summary

In today’s world, document data have become a routine part of everyday business operations. Logistics organizations are constantly looking for ways to improve document processing and automation, as these tasks currently occupy a large number of human resources and time. Document automation powered by artificial intelligence is a way that document processing can be improved to reduce errors and increase productivity.

Processing documents faster, eliminating human errors, and saving costs on document storage are just a few of the benefits document automation powered by machine learning offers. IDP can provide further benefits when applied in a customized way to cover wider workflows and business operations. With easy document storage and document reports, document automation is a definite yes for logistics companies.

About the author

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams. She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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