Introduction

AI is revolutionizing how businesses optimize their operations, and document management is no exception. Despite significant advancements in digitization, many companies still encounter persistent challenges in standardizing, automating, and integrating their document processes. Leveraging an AI-driven e-document framework could address these challenges, streamlining supply chain exchanges like never before.

At the recent PIDX conference, Zakhar Dikhtyar, CEO of DocStudio, unveiled this innovative technology and its transformative potential for supply chain document management. In this article, we’ll delve into the key insights shared during the presentation.

Document Automation Challenges in Supply Chain

Incomplete EDI Integration

Many supply chain documents are already structured and standardized, making them ideal for automation. Common examples include purchase orders, invoices, advanced shipping notices, bills of lading, goods receipts, inventory reports, requests for quotations (RFQs), and delivery orders. For a large number of companies, automation occurs through EDI (Electronic Data Interchange). 

However, while 59% of U.S. supply chain companies use EDI, 41% still depend on manual processes or non-automated systems (DataInterchange). This gap exists because not all partners have the infrastructure for seamless EDI integration, creating barriers to full automation. 

Management of Legal and Operational Documents

In addition to these structured documents, the industry also handles legal and operational documents, which often lack standardization. These are typically exchanged inefficiently in unstructured formats like PDFs and paper-based forms, posing additional challenges. Examples of such documents include NDAs and contracts, compliance forms, partnership agreements, service level agreements (SLAs), employment contracts, and terms and conditions.

Difficulties with Document Recognition

Another important challenge arises with document recognition. Let’s take the example of invoice automation. Common difficulties with invoice processing include complicated layouts and designs, misinterpreted data fields (e.g., invoice numbers, quantities, and prices), handwritten notes, inconsistent decimal formats, and the lack of structured data after OCR processing. Additionally, manually matching items to ERP systems remains a significant hurdle.

Achieving full digitization of supply chain, legal, and operational documents offers numerous advantages. These include reducing document-related costs by up to 40%, significantly cutting salary-related expenses, and halving the time required for contract processing. Additionally, up to 80% of the time spent on file sharing can be eliminated, driving efficiency and streamlining operations (SignHouse). Adopting AI technologies is a significant step towards achieving full document automation.

Solutions for AI-Driven Document Management

AI-driven document management helps companies to overcome the common challenges in document automation. There are three key aspects to it:

• OCR (Optical Character Recognition): Extraction of text from scanned documents, converting them into editable formats.
• Structure Detection: Identification of critical elements like invoice numbers, dates, and amounts, validating data with logical rules (e.g., Quantity × Price = Total).
• AI-Based Item Matching: Natural language models analyze product descriptions to match invoice items with ERP entries. The system learns from human corrections to improve over time.

Several solutions on the market offer document structure recognition. Examples include DocParser, DocuPhase, UIPath, and DocSumo. These are available as web platforms, APIs, or applications for iOS and Android. Some options, such as SAP and Dynamics 365, provide integrations with ERP systems. However, none of these solutions offer item matching, nor do they integrate with existing EDI (Electronic Data Interchange) or DMP (Document Management Platforms).

At DocStudio, we went through a process of trial and error before developing the most optimal AI document recognition framework for supply chain documents. It has already been tested by both our team and numerous clients, proving its efficiency. Let’s dive into how it works.

The AI Document Automation Framework by DocStudio

The framework uses two AI models: the Document Structure Detection Model and the Item Matching Model. These models work together through a series of steps designed to efficiently process and manage supply chain documents.

Document Ingestion: The process begins with the ingestion of a large volume of documents, such as quotes, invoices, orders, and specifications. These documents arrive through various channels (web portal, procurement platform, FTP, email, etc.) and in different formats (PDF, DOCX, PNG, JPEG).

OCR Processing: For image-based files (like PNG or JPEG), Optical Character Recognition (OCR) is used to extract text-based content. This step converts scanned images or photos into machine-readable text.

Block Detection and Decomposition: After text extraction, the system identifies key data blocks within each document. This includes recognizing specific fields like document type (e.g., invoice), document date, and sender information. These identified data blocks form the basic structure for further processing.

Data Storage: The extracted and structured data is stored in a standardized format (e.g., PIDX), ensuring that it is organized and accessible. However, at this point, the system has not yet matched the items listed in the document with the corresponding items in the ERP system.

AI-Based Item Matching: This is where AI comes into play. The AI-based matching tool analyzes the items listed in the document and attempts to match them with the corresponding items in the sender’s inventory or ERP system. This process improves accuracy over time as the system learns from past matches.

Approval Process: After item matching, the documents, particularly invoices, are sent to a responsible person for approval. If the AI system has made correct matches, the process is quicker and more efficient. Incorrect matches are flagged and used to retrain the AI, improving its accuracy and reliability in future processing.

ERP Integration: Once the items are accurately matched, the recognized and validated documents are sent to the company’s ERP system in the appropriate format (e.g., EDI). This step ensures seamless integration with existing enterprise systems, allowing for smooth processing and handling of transactions.

At the initial stage, and each time there are significant changes to the document type set, design, layouts, or major updates to the item list, the accuracy of the AI models needs to be reevaluated. If necessary, the models should be retrained using the new data. This process may be ongoing, but it requires a supervisor to act as a quality judge for the system.

If you’d like to learn more about how this framework works and how it can be implemented, click on the video below to watch the recording of Zakhar Dikhtyar, CEO of DocStudio, presenting at the PIDX conference.

Key Takeaways

AI-driven systems can automate up to 95% of document-related tasks, drastically reducing manual efforts. These solutions require minimal changes to existing IT infrastructure, making them accessible to businesses of all sizes. While human involvement remains necessary for quality assurance, advancements in AI will likely reduce this dependency in the near future. From invoices and purchase orders to contracts and SLAs, AI-powered frameworks offer a unified solution for managing supply chain documents. As standards and integration capabilities evolve, these systems will become indispensable for efficient, secure, and scalable workflows.

Would your business benefit from an AI-driven document management solution? Reach out to the DocStudio team at hello@docstudio.com or fill out the form here to discover how we can help streamline your operations and support your business growth.

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