Use of AI-Assisted OCR vs. LLM for Supply Chain Documentation: What Is the Difference?

Efficient E2E supply chain management requires more than swift logistics and real-time data access. Handling documentation with effective process automation is an essential part of maintaining a competitive edge in today’s global economy. Two technologies – OCR and LLM – are used in the realm of intelligent document processing (IDP). The legacy solution, OCR, is commonly used for these purposes across multiple industries. However, it does not provide all the benefits of emerging AI technologies like LLM.

First, understand what each of these emerging tech options entail. Then, explore their pros and cons to determine the best path forward for your company. No matter what path you take toward a successful future, AI assistance will help you leverage existing efforts and improve every aspect of supply chain management.

The Technology Explained

Before choosing the best solution for supply chain documentation, it helps to understand what OCR and LLM are and how they function.

What Is OCR?

OCR stands for Optical Character Recognition. This tech is used to convert printed or handwritten text or scanned images into data that a computer can use. After scanning the physical item, software can recognize letters, numbers, and symbols and make sense out of it.

Machine learning (ML) enhances OCR by allowing it to adapt to different input options (i.e., fonts, text sizes, handwriting, blurry images, etc.) and use multi-phase processes to remove or minimize potential issues.

What Is LLM?

LLM stands for Large Language Model. This type of program uses AI to understand text and generate fresh content. They use neural networks and massive data sets for training to increase accuracy and efficiency. For supply chain management, the LLM could be fine-tuned on industry knowledge.

Pros & Cons of OCR and LLM in Supply Chain Management

Organizations of all kinds need to stay on top of the ever-changing world of supply chain management. Advanced technology like artificial intelligence has become a pivotal and expected part of business operations. With OCR and LLM at the forefront of robotic process automation (RPA) for documentation, you need to know more to make smart choices. Each has diverse benefits and disadvantages to consider.

OCR

Optical character recognition works when it scans and processes expected information. It can recognize certain types of data in defined fields like a SKU, purchase order number, and order quantities. However, in order to get to that point, employees must give annotated examples of document inputs in the first place. When multiple customers use a variety of different forms or document types, the system struggles. Success rates hover around 50% for standard OCR systems.

When OCR is combined with machine learning, the results are quicker and more accurate if the organization takes time to train more fully. This requires a considerable amount of user input upfront. While the outcome is better, some companies may not have resources available to do this. In-house data is required for effective training.

This is one of the more prominent disadvantages. Each document type and customer need a set template that suits the OCR data validation system. If anything changes, the system may not be able to read and process the document accurately. These difficulties can stem from things such as structural differences, length, number of items, font variations, clarity, and similar factors.

Consider this example of OCR process automation gone awry. A company using AI-assisted tech has been working with a particular supplier for over a year. They have established a set document template style that works with the in-house system. Then, in a burst of holiday spirit, the supplier adds a line that says “Merry Christmas and Happy New Year” to the top of each document.

The automation does not know what to do with this new information and the resultant shift of positioning and layout for the expected data on the purchase order. Errors accumulate. Work slows down. The team is left scrambling to handle data entry and processing manually. These issues are especially hard to bear during the busy season.

LLM

The issue of unexpected data or changed templates mostly disappears when a company uses an LLM for supply chain documentation management. These AI systems comprehend the document itself in a more holistic way. They do not simply look for specific fields or data types defined by user input during the training process.

This means that you will only need one template per document type, but changes to the structure will not get in the way of comprehension or data handling. These different approaches may include number of pages, length of the document, or added text like the holiday greeting mentioned above. Also, the ability to process information outside of the template makes things even more powerful. An LLM can process a quantity change or updated SKU code from an emailed purchase order, for example.

At this point, the main disadvantage of adoption focuses on a lack of AI expert talent. This is a great reason to use an established tech provider with a system already in place for you.

What Option Works Best for Your Company?

LLMs offer more advantages overall for supply chain document management. Although OCR provides many benefits over manual data entry and use, it still struggles with any changes and does require more user input. LLMs offer more speed, agility and resilience when it comes to handling document changes, and improved capability for multiple use cases.

Perhaps best of all, LLM process automation does not need as much user input and updates as other options. They are ultimately much more user-friendly, easier to learn, and more comfortable at every stage of the organization’s operations. This can help even the most tech-resistant executives accept them more readily and more traditional employees learn how to use them in less time.

No matter what specific option you choose, building effective and efficient workflows helps everyone get on board. These efforts also ensure the highest degree of accuracy for all results. LLMs for E2E supply chain management and document handling can revolutionize the competitiveness of any company’s efforts.

Curious how it works in practice? Get ready to explore the benefits of AI-based E2E supply chain management when you try it for yourself at extrakt.AI for free.