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Natural Language Processing (NLP) in Smart Manufacturing

New Ways to Talk to Machines

Natural Language Processing (NLP) is a rising star in the world of data science, pushing the boundaries of what machines can understand and interpret. Despite its growing popularity, the practical details, such as using these models in production, are often overshadowed by the interest in the models. This article aims to fill this gap and provide insights on how to successfully apply NLP models in a production environment.


An image depicting natural language processing
Visual Depiction of Natural Language Processing

What is Natural Language Processing (NLP)?

"Natural Language Processing" (NLP) is a technology that combines artificial intelligence and linguistics techniques to understand and process the complexity of human language. Its main goal is to enable machines to understand, interpret, and even produce human language. This goal is achieved through various applications such as text analysis, language translation, sentiment analysis, and speech recognition.

Currently, NLP technology is particularly used in areas such as customer service, market research, and social media analysis. Chatbots, virtual assistants, and automated translation services are excellent examples of how NLP is integrated into our daily lives.

Growth in the Natural Language Processing (NLP) Market

The NLP market is a rapidly growing industry focusing on developing technologies that enable computers to understand, interpret, and generate human language.

The NLP market is valued at $29.1 billion in 2023 and is expected to reach $92.7 billion by 2028, growing at a compound annual growth rate of 26.1%.

North America has a 36.1% share of the NLP market, followed by Asia-Pacific and Europe.

You can refer to this market research for more data.

The Power of Textual Data

Despite significant progress in the digitization of the industrial sector, data analysis is often limited to numerical data, and the synthesis of knowledge from textual data is neglected. This is where NLP comes in. By processing textual data, NLP can structure knowledge within a company and use this knowledge to improve business processes by translating employees' experiences and observations into organizational strategies.

Current Use of NLP in Manufacturing

NLP has started to be used in some critical areas in the manufacturing industry, although it has not yet shown its full potential. For example, systems that enable workers on manufacturing lines to control machines with voice commands are an application of NLP technology. It also provides meaningful insights by analyzing large datasets about manufacturing processes.

The Future of NLP in Manufacturing with Advancing Technology

Enhanced Communication and Collaboration

NLP enables more fluid and meaningful communication between machines, sensors, and operators, helping manufacturing processes become more synchronized and efficient.

Predictive Maintenance and Failure Analysis

Combined with machine learning and data analysis, NLP can predict equipment failures and proactively identify maintenance needs. This capability increases operational efficiency by preventing unexpected breakdowns and manufacturing downtime.

Here are some examples of how NLP can be used in predictive maintenance and failure analysis:

Analysis of Error Reports

NLP can analyze error reports from machines and sensors. These reports often contain technical terms, abbreviations, and industry-specific jargon. NLP can understand these texts and extract meaningful information (error codes, operating conditions, error frequency).

Processing Maintenance Notes

Technicians' maintenance notes are usually written in accessible formats and natural language. By analyzing these notes, NLP can identify common problems, potential risk factors, and maintenance recommendations for specific equipment.

Trend Analysis and Anomaly Detection

NLP can detect abnormal behavior (anomalies) by analyzing data trends over time. For example, increasing complaints about a particular piece of equipment or the frequent recurrence of a specific error code can be early warning signals of potential problems.

Management of Knowledge Accumulation

NLP can build a body of knowledge by combining information from past maintenance reports and fault records. This knowledge helps to make more informed future maintenance decisions.


Customized Production and Customer Interaction

NLP can play a critical role in customizing products and services by analyzing customer feedback and market trends. This increases customer satisfaction and makes production processes more flexible and demand-responsive.

Training and Knowledge Transfer

NLP can facilitate the training and knowledge transfer of new employees through virtual assistants and training platforms. This advantage accelerates the learning process and enables employees to adopt new technologies and processes faster.


Natural Language Processing technology has the potential to revolutionize the manufacturing industry. Enabling machines to understand and interpret human language is essential to making business processes more efficient, flexible, and innovative. The possibilities NLP offers will shape today's and tomorrow's manufacturing worlds. Therefore, industries must embrace this technology and fully explore its potential to gain a competitive advantage and achieve sustainable growth.

We have come to the end of our article. I hope it was enjoyable and informative.


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