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Importance of NLP in Manufacturing and Criticality of Data Labeling

Jun 24, 2022

The NLP solutions are highly adopted by the industries such as manufacturing, healthcare, automotive, and advertising. With the rising demand, the market revenue for the globalNatural Language Processing industry is predicted to cross USD 127.26 billionby the year 2028. This shows the thriving demand for NLP in various industries. But its adaptation is highly appreciated in the manufacturing industry. It is one of the major digital technologies that has emerged recently in the industry.


NLP is considered a sub-field of AI technology, which promotes interactions between human or natural languages and computer languages. Specifically, NLP is used to program computers and prepare them for processing and analyzing large amounts of data in association with natural language.

With NLP, AI can be programmed to imitate human speech and form natural sentences to improve human-machine interactions. Data Labeling is another integral part of NLP utilization, which programs the NLP models to understand and generate speech, just like humans.

This article focuses on the utilization of NLP in the manufacturing industry and the implementation of data labeling functionality in its utilization.

Employment of Natural Language Processing in Manufacturing Sector

Natural Language Processing (NLP) is an interactive algorithm that bridges the communication gap between computers and humans. The computers learn the feasibility of understanding human speech and responding with specific actions.

The implementation of NLP in the manufacturing industry is carried out through specific processes, which include:


  1. 1. Process Automation


The use of NLP automates several information processing approaches and helps in streamlining the execution of several repetitive tasks. Some tasks, such as report analysis, paperwork, and others, are automated and require less manual effort.


  1. 2. Management of Inventory


The manufacturing industry needs to run a constant analysis of sales, user reports, and stocks of specific products to make ideal decisions to run the company. It helps them maximize profits and optimize the business operations.

With the help of NLP technologies, the process of inventory management becomes comprehensive, and the error possibilities are also reduced during sales analysis.

The manufacturing businesses are finding it easier to analyze the available products and discard the same that are of low quality. Hence, it maintains the sales flow and supply chain of the manufacturing business.


  1. 3. Operation Optimization


NLP tools are fed with algorithms that are accountable for tracing the equipment performance. It can help notify the business whether the equipment is 100% efficient in its operations or not.

NLP implements real-time monitoring of such details of the industrial machinery. Following that, the businesses can take ideal measures to improve the operating potential of selected machines.


  1. 4. No Middle Man Operations


The organizations in the manufacturing sector hire data analysts for proper in-depth data analysis, especially during multiple manufacturing processes. Hence, their job was to keep record of the machine readings, reporting operational changes and to identify the gaps.

Such manual operations by data analysts cost a lot of time to the manufacturing businesses. Therefore, as a remedy today, manufacturing businesses are using robotic sensors embedded with NLP to serve the same purpose.

They are deployed to keep track of operations and timely report the fluctuations or errors, and update it to the management system. Hence, no middleman or data analyst is needed for the job anymore. Automating this aspect will ensure that any possible damage is caught beforehand to save a lot on repairing cost.

The Use of Data Labeling for Escalating the Functionalities of NLP Technology

Data annotation or data labeling is the key part of realizing the full potential of NLP, the accuracy and efficiency of labeled data are sure to make a big difference in the final results obtained from the AI algorithm model.

Manufacturing businesses are using machine learning models for predictive maintenance, and smart manufacturing including production line, quality inspection and warehouse logistics.


When it comes to large manufacturing processes, there is possibility of errors in the types of data used for training AI models.

Manufacturers looking for intelligent transformation and the right technology expertise must get assistance from training data service providers who have in-depth knowledge and vast experience to figure out the data labeling instructions and get more reliable and suitable data.


On the other hand, the research and development cycle can be reduced with the help of high-quality data in special cases of manufacturing data labeling. Also, this can help in accelerating the implementation process, allowing manufacturing businesses to make faster and more intelligent transformations.

Manufacturing businesses can transform the way they conduct data labeling with the help of NLP powered tool- UBIAI. Where accurate and in-house data labeling can prove to be costly for manufacturing and other businesses, a tool such as UBIAI can be the best choice to make. It will transform your unstructured data into organized and usable data, allowing you to get the right insights. UBIAI is an all-encompassing tool with extensive features that support user experience and seamless data labeling. When compared with other tools, the user interface of UBIAI is fully optimized; the easy to use interface simplifies the entire labeling process.

Perks of UBIAI on High Standard Auto Labeling

The NLP data labeling demands high consistency and accuracy, and UBIAI features on auto labeling solutions have the potential to provide that. UBIAI has the feature of allocating dictionaries to annotate and auto-label the words. These dictionaries will assist with entity labeling and will pre-annotate every piece of data.

Moreover, UBIAI also promoted auto-labeling of the documents using ML models. It consists of fine-tuned models which are tested for performance. Following that, each entity’s recall and precision score will be displayed. After the model training is acquired, you will get the option to export the model and use it for your application.

Besides the other ways, UIBAI allows rule-based labeling of all documents by assessing multiple valid rules. These rules should be pre-defined with number/text patterns, Part of Speech (POS), and regular expressions, which will enable instant labeling of all documents that one uploads.

Check out the website Ubiai.tools to get more detailed insight into the auto labeling feature. You can also book a demo to explore the service perks of this solution before you can start using it for your manufacturing business and NLP requirements.