Use of NLP in Supply Chain

Jun 7, 2022

With continued globalization, supply chains are getting smarter in using advanced technologies to optimize their processes. However, the Covid-19 pandemic has posed unprecedented challenges for supply chains such as labor shortages, delays in transportation, shortages of raw materials, unplanned operational shutdowns, and a sudden rise or fall in demand. Such crisis situations can strike without any prior notice, and this is where supply chain organizations can drive the processes efficiently with the help of NLP.  

Natural Language Processing (NLP) utilizes artificial intelligence (AI) and machine learning (ML) to help computers analyze and understand human language. Use of NLP makes supply chain operations simpler and more coordinated. 


  • Automation: When it comes to making the right logistic improvements, thousands of shipment documents can be read through NLP, which also helps gather valuable summary information. 
  • Track real-time changes: If you want to maintain master data with total accuracy, NLP algorithms can help track changes in real-time.  
  • Obtain industry benchmark data: From labor costs and fuel prices to transportation rates, web scraping can be utilized to obtain various kinds of industry benchmark data. This can help organizations to improve their performance, and also figure out the opportunities to save costs.  
  • Verify compliance: If there is any potential breach by suppliers, NLP can help in the verification of compliance through ethical practices by web scraping information that is available to the public.  
  • Translating documents: Today, almost every business is operating globally, and language barriers can adversely affect process efficiency. These language barriers can be reduced to a great extent by translating a document into different languages with the help of NLP. 

Parse, Analyze and Report using OCR Technology


One of the most common and highly effective text recognition techniques, OCR (Optical Character Recognition) offers excellent results. From scanned documents and PDFs to images, OCR helps in converting various documents into editable and searchable data. From scanned documents, various businesses still extract data manually, which is an expensive and time-consuming process. OCR technology helps in extracting, analyzing, and reporting data without having the need of manual effort. Whether you want to extract data from any invoice or report, the document processing can be quickly automated, enabling you to take the necessary actions on the extracted data.


Extract Data using Custom Fine Tuned Models


Revolutionizing the NLP space since its inception, transformer-based language modes are highly advanced models that rely on transformers, and for all NLP and NLU tasks, these models are now recognized as state-of-the-art models. There are countless transformer-based and other models available in the domain. They differ from one another due to various factors including their datasets, hyperparameter selection, their architecture, and their training objectives. For example, GPT models were trained on WebText data and links in Reddit comments are the place they are mainly scraped from. On the other hand, Google’s BERT was completely trained on the corpus of English Wikipedia. These two recognizable models differ from each other in their pre-training objective as well as training datasets. As the style of language that is utilized to train these pre-trained models can be different from one another, they may have their specific application needs.

What fine tuning means here is, a pre-trained language is re-trained using your own customized data. The fine tuning procedure results in updated weights of the original model, to account for the characteristics of the domain data, and the tasks that you are more likely to perform.


Query Supply Chain Data Information


Every modern supply chain has large datasets that can unlock insights. NLP allows users to directly ask questions of the data. While analyzing data from various applications and sources, supply chain data analytics helps in uncovering insights. Data can offer deep insights about all the links in the chain. From warehousing and fulfillment to order management and shipping, it includes every aspect of the supply chain.

As the modern supply chain is full of complexities, there are various points where some kind of failure is possible. If a single link in the chain gets affected and faces a shutdown, it can adversely affect the entire system. This is where supply chain analytics can prove to be helpful. They help businesses to figure out where the possibilities of some problems exist, and how they can prevent these issues from happening in the near future. No business would want their supply chain being affected and analytics can help in resolving issues as soon as they strike. Also, it can help in streamlining the entire supply chain process, and also improve it as per the changing requirements.


Role of Data Labeling in Creating Custom Models

A large volume of top-quality training data is a prime necessity to build the right machine learning models. But, this process can be time-consuming, a little more complicated, and needless to mention, very expensive. When it comes to making the model learn how to make the right decision, human efforts are required to label the training data.
The right solution helps in labeling workflows for humans with the help of which they can conduct segmentation labeling jobs, do the text classification, and object detection. It is also possible to build custom workflows if you want to define the entire user interface for all kinds of data labeling tasks.
The right partner will help you in every aspect of labeling jobs, some of them will also allow you to build your own HTML for the UI.
Some reasons you may need to build custom workflows 
  • Your data labeling requirements are highly specific and need attention to detail.
  • Your input data has various elements and there are various complexities.
  • While creating tasks, you don’t really want some items to be sent to labelers.
  • With hopes to improve accuracy and consolidate labeling output, you are in need of custom logic.
Let’s take an example of a science conference. They have hundreds of thousands of abstracts that are reviewed manually. From backgrounds and objectives to methods, complications, limitations, and results, a general abstract of a science paper can include different types of information.
Imagine how troublesome it can be to review all these sections for thousands of abstracts.

How UBIAI can Help Automating Data Extraction for Supply Chain



UBIAI provides cloud-based solutions and services in the field of Natural Language Processing. It is one of a kind solution that can help in data extraction for supply chain. With UBIAI’s tool, you can extract data from simple text files, receipts, invoices, or other structured or non-structured documents. By automating the entire extraction and labeling process, UBIAI can help in saving a great deal of precious time while streamlining the entire business process.
Some key things to know:
  • Exceptional OCR Capability
What does it take to parse text from PDF, scanned documents, and images when you have UBIAI? You guessed it right… a simple drag and drop will get the job done for you.
  • Save your Precious Time
When it comes to auto-labeling of date, annotation can be done directly on native PDFs and training a machine learning model, and hence, it saves a great deal of your precious time.
  • Train your Model Externally 
The right format matters! Feel free to download the model or export in the right format, and train your model externally.
To know more about the tool and to implement the same in your business, feel free to connect with our dedicated professionals who will help you from the concept to completion. Visit now!