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September 23, 2021 | 14:09

Document and data management illustration.

Documents are everywhere: from business to membership applications and charity donation forms. Documents exist in different industries and various volumes. And businesses always struggle to process these documents with efficiency and speed, especially semistructured and unstructured ones. Intelligent document processing is a way out. It automates the processing of data contained in documents ― deriving what the document is about, what information it contains, extracting that information, and sending.

In this article you will learn everything you need to know about intelligent document processing in supply chain businesses.

What is intelligent document processing (IDP)?

Document processing illustration.

Intelligent document processing is a complex business solution with deep learning tools that automate the processing of intelligent documents. Intelligent document processing uses RPA bots, AI, and computer vision to extract unstructured data from documents (e.g., email texts, PDF, and scanned documents) and convert it into structured ones.

Intelligent Document Processing in the supply chain helps to:

  • Eliminate manual interventions in the document-driven workflows
  • Improve data quality and reliability as human-prone errors get excluded
  • Reduce document processing execution time, resulting in decreased operational costs
  • This technology is often combined with Robotic Processes Automation and Optical Character Recognition. However, survey results from our Automation with Intelligence report reveal that intelligent automation uptake is driven by the expectation of increased productivity and cost reduction, greater accuracy, and an improved customer experience. Executives estimate intelligent automation will provide higher increases in revenue as a result of their automation to date, compared to those using RPA alone (8.5 percent versus 2.9 percent).

    With IDP in supply chain management, organizations can transform business processes – integrating more intelligent technologies to automate document-driven use case targeted opportunities.

    According to Gartner, “Intelligent document processing is increasingly important to create operational efficiencies in business processes that need to extract information from semistructured and unstructured data for further analysis.” Moreover, the global intelligent document processing market was valued at $765.2 million in 2020 and is projected to reach $6.8 billion by 2027 with a CAGR of 35.4% during 2021–2027.

    What drives a document processing market, and what’s the role of intelligent documents in supply chain management? It’s all because of the growth of the amount of data generated by enterprises and the need for companies to process large volumes of documents with greater speed and accuracy. Let’s learn more about the main pillars of intelligent document processing. 

    The three pillars of intelligent document processing

    Three main pillars make up intelligent documents in supply chain processing: machine learning, optical character recognition, and robotic process automation.

    IDP and RPA  - the major components of the automation workflow.

    Optical Character Recognition

    Optical Character Recognition is a niche technology that recognizes handwritten, typed, or printed text within scanned images and converts it into a machine-readable format. To better understand how it works, let’s see how FineReader works:

  • Processes the document images, analyzes their structure, and divides the page into blocks of texts, tables, and images
  • Compares the singled out characters with a set of pattern images
  • Sets hypotheses about these characters
  • Analyze how lines break into words and words break into characters
  • After processing a large number of hypotheses, the program shows the user a recognized text
  • As you have got, OCR just pulls out the textual part of the image without understanding the context. For this purpose, machine learning is used.

    Machine Learning

    As document processing is a very resource-consuming task, most companies decide to automate this process. For example, Dropbox has recently launched HelloWorks, a product that turns PDFs into mobile-friendly forms. With the help of machine learning, it automatically processes pdf documents. Another example is Intuit that uses machine learning to process documents and eliminate manual data entry for their customers.

    Robotic Process Automation

    Robotic process automation is a software solution designed to process large volumes of data. With the help of RPA, a “robot” captures and interprets existing applications for manipulating data, triggering responses, and communicating with other digital systems. Its capabilities include:

  • Open email and attachments
  • Log into web/enterprise applications
  • Move files and folders
  • Copy/paste information
  • Fill in forms
  • Read/write to databases
  • Scrape data from the web
  • Connect to a system API
  • Extract structured data from documents
  • Collect social media analytics
  • Follow “if this, then that” rules
  • RPA can be enhanced with AI-based document processing to improve the robots’ skills and accuracy.

    The key stages of the IDP process in supply chain

    Intelligent document processing consists of four phases to get data from unstructured and semi-structured documents: pre-processing, classification, extraction, and post-processing.

    The key stages of the IDP process in supply chain management.

    Document collection

    The documents are classified and categorized into different types during the first step and then made ready for conversion. IDP is designed to integrate, validate, repair/impute problems, split images, organize, classify, and enhance images during this step.

    Document preprocessing

    Document preprocessing is the process of incorporating a new document into an information retrieval system. During this stage, techniques such as noise reduction, binarization, and de-skewing are applied. The goals of document preprocessing are to improve the document space for its storage and reduce the time for processing retrieval requests.

    Document classification

    Documents usually consist of various pages in different formats. In this case, AI-based technologies are used to classify and separate multiple documents. As a result, they extract the relevant pages. For example, a mortgage application consists of multiple pages with loan documents, invoices, and bank statements that must be identified and classified.

    Data extraction

    During this stage, OCR and ML technologies digitize documents and extract specific data. IDP solutions contain a library of pre-trained extraction models, pre-populated with the right fields for extraction that extract data from the documents.


    With the help of external databases and human-in-the-loop machine learning, IDP platforms validate the data extracted from documents. In such a way, the problematic data is corrected by humans. It helps to ensure the data quality and collect the data in the right format.


    After all previous stages are completed, the validated data is integrated into large enterprise systems and workflows.

    IDP technology implementation scenarios in supply chain

    IT security and cloud services illustration.

    Depending on your business specifics and requirements, you can use the following scenarios of IDP technology for different types of intelligent documents.

    #1: Work with easy-to-convert documents with little to no variation

    If you work with simple and not too complex documents, you may use the services of legacy OCR and RPA providers that also have IDP offerings. Such an approach will save your costs compared to IDP-focused or custom solutions. The examples of RPA and OCR vendors with IDP capabilities are:

  • Automation Anywhere – AI-powered cloud RPA platform
  • ABBYY – a digital intelligence company with an intelligent document processing platform
  • UIPath – RPA platform that provides intelligent document processing solution
  • Blue Prism – a platform that helps to extract data from different types of documents: structured and unstructured
  • #2: Deal with large volumes of complex, mostly unstructured documents

    If you deal with large volumes of complex data, you’ll need more advanced solutions that use artificial intelligence and machine learning. Take a look at the out-of-the-box solutions with more advanced IDP functionality for demanding automation challenges.

  • Infrrd – IDP-focused solution enabling high-quality data extraction from complex, unstructured documents
  • Hyperscience – ML-powered IDP tool that can classify and extract data within complex documents such as PDFs, emails, handwritten forms, images, and more
  • Rossum – an end-to-end intelligent document processing solution that helps organize and automatically process incoming documents of different formats
  • #3: Run a niche business with a unique document workflow

    Sometimes, companies have specific tasks and complex document workflows that can not be solved with ready-made software. In this case, custom solutions are the best choice.

    Benefits of EDP in Supply Chain

    An illustration of the delicate food supply chain.

    EDP can automate and improve various business use cases across all industries where documents are still processed manually. It includes horizontal use cases, such as invoice processing and purchase order processing, as well as industry-specific use cases, such as insurance claims, loan applications, know your customer (KYC), and others. The benefits of using EDP in intelligent automation in supply chain are:

    Zero-Latency Exchange of Documents

    Exchange of documents allows your team to improve the instant transactions between different units and capitalize on freed-up time to focus on business processes more.

    Highest Accuracy

    As no human intervention is required, the process of document processing becomes more accurate. It allows companies to eliminate human error and define the standard in which the easy-to-convert documents are supposed to be created.

    Lower Costs and More Time Efficient

    You can lower your processing costs with AI-driven IDP + RPA that improves straight-through processing (STP) by continuously learning from human feedback. Companies used to spend a lot of time on communication and sharing documents between different parties. With EDI, companies no longer have to worry. EDI solution allows companies to communicate electronically and process requests instantly, without any human error and need to check documents manually.

    Greener Business Potential

    More companies strive to become paperless. IDP is one of the ways to streamline this process. Choose an efficient EDI software or go for a custom solution to improve the processes and forget about all the manual paperwork.

    Better Relationships and Sales

    IDP makes business processing cycles faster and smoother, turning you into a more reliable partner and improving your customer experience.

    Streamlined Inventory Management

    Smart EDI software notifies you in case of shortages or over-storage. With EDI, you can use your inventory space more efficiently and better control the logistic processes.

    How much will the IDP implementation cost?

    According to Capterra, Intelligent Data Processing pricing starts at $3000 per user per month. Of course, it depends on the volumes and complexity of data you want to process, so in general, the prices range from $2800 to $4200.

    Before deciding on an IDP solution, you should consider the following:

  • There should be a technological environment that allows you to utilize intelligent automation’s capabilities fully
  • You can consider vendors with fixed-price solutions where you can process as many documents you like from one machine for a fixed fee or find a pricing model based on the volume and complexity of documents
  • Ensure that a solution not only processes the documents automatically but also can identify a document or field that it cannot read and send it for human involvement
  • Consider developing a custom solution if you want to get a solution tailored to your specific needs and adjusted to the complexity and volume of documents you want to process. The prices for it can range from $15,000 to $60,000. Here are the benefits you’ll get:

  • Converting images or documents into digital formats you need
  • Minimal training needed for minor changes
  • With the help of NLP, a system can process any volume of structured and unstructured documents and create summaries
  • How Amсonsoft helps in developing IDP module

    Amconsoft has experience in implementing the IDP module for the logistics platform – Inn.Logist. We have worked with multifunctional logistics services for automating and optimizing operational activities. Its goal is to help make profitable transactions for various participants of the logistics process.

    Inn.Logist is the advanced cloud platform for logistics companies to manage operational processes.


    Our task was to implement an IDP module that will collect and process data from different logistics partners and place it into one comprehensible dashboard for service providers and clients.


    Based on Machine Learning technology, we have developed a system that distributes the orders from different service providers to specific participants. The system contains a single-price format that collects data from all partners and extracts information about their pricing and quality. 

    With the help of this module, companies can track the work processes of drivers, track the location of both your and your partner’s transport, manage documents in one dashboard, receive alerts, use templates and electronic signatures.

    If you are interested in developing a custom IDP solution, don’t hesitate to contact us. We at Amconsoft provide a free consultation to guide you on the main types of such systems and the features you need for your business needs.


    Views: 661

    Written by:

    Filonenko Vyacheslav

    I've been leading a tech department specializing in eLearning applications and Business Intelligence for 10 years

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