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
- 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.
How UBIAI can Help Automating Data Extraction for Supply Chain
- Exceptional OCR Capability
- Save your Precious Time
- Train your Model Externally