The Global Market Revenue of Natural Language Processing is forecasted to be US$8,319 million, with a CAGR of 18.10% between 2019-2024. 

Traditionally, data was processed manually. Organizations faced the challenge to dispose of a huge amount of data that was generated regularly. Stored in hard-drives or files, this data was either ignored or discarded once the production was completed. But in modern times, this data can be extensively useful to organizations. Certainly, the data generated presently is thrice than earlier. Reports suggest that 463 Exabyte of data is generated daily. By the end of 2020, researchers have estimated that each person will generate 1.7 megabytes of data every second.

This data is unstructured, unwanted and hefty. Businesses need insights from this data so that decisions are made from the real-time information rather than just on predictions. Since this data is unsegregated, the traditional methods cannot rule out powerful insights. Here is where Natural Language Processing comes into the picture.

Understanding Natural Language Processing

Natural Language Processing is a subsidiary of artificial intelligence that bridges the gap between human behaviour and technology. With the ubiquitous data available, this technology identifies the key elements from human instructions, extract relevant information and then process the said information in a manner so that machines can understand it.

This is exactly how Amazon's Echo, Microsoft's Cortana and Apple's Siri functions. Moreover, integrating NLP into the system assists machines to understand human language and mimic human behavior.

Though NLP is yet evolving, it has been utilized since the early 1960s in the search engines. Analytics Insight has forecasted the Market Revenue of Natural Language Processing to be US$8,319 million, with a CAGR of 18.10% between 2019-2024.

Eliminating Middle Man

Organizations employ data scientists or data analysts to analyze the data, during multiple manufacturing processes. This includes keeping track of the machine readings, identifying gaps and then reporting about any changes or discrepancies in the operations. As these processes are governed by traditional methods, they become time-consuming and delay the reporting to the managers for any loopholes in the manufacturing process.

To rectify this concern, robotic sensors using NLP can be deployed in the manufacturing plant, to keep an eye on the operations, and report discrepancies directly to the management without any involvement of the middlemen. This ensures that timely action is taken so that cavernous damage is not done in the manufacturing process.

Process Automation

Integration of NLP in the manufacturing process ensures that the repetitive and mundane jobs like paperwork and report analysis gets limited. Moreover, with automation, it will help in the seamless workflow without disruption, and will allow employees to focus on the tasks that require human skill sets.

Inventory Management

In the manufacturing sector, scrutinizing data about the sale of products is equally important along with the production. Traditional methods make this task not only challenging but a slight discrepancy in analyzing the sales can cause huge money to the organization. By integrating NLP, this entire process becomes thorough and comprehensive.

Moreover, the organizations can analyze areas which are lacking behind sales, and chalk out a strategy to rectify it. By implementing NLP the organizations can also analyze the quality of the product manufactured. This helps in discarding those products that lack the quality so that the supply chain, and sales do not get affected.

Understanding Customer Behavior

Like mentioned earlier, analyzing historic data, is one of the best ways through which the pattern in customers behaviour can be determined. By integrating NLP into the manufacturing system, insights can be drawn about the past purchases of the customer. This aids the organizations to chalk out strategies that will be conducive to market new products and services according to the customer behavior.

Accessing the Damage in Manufacturing Plant

There are some areas in manufacturing plants, where loopholes cannot be observed by the human eyes. These areas are sensitive and become a threat to both human life and organizations, if not addressed timely. By integrating sensors with NLP or camera with NLP, the problems can be controlled timely, and without human intervention.

For example, deploying a sensor with NLP in the chemical processing area of the manufacturing plant will notify the manager when the amount of chemical is more than normal.

Outlook

NLP is an area with extensive potential. But its operability also demands a huge amount of training and skill set. Once these concerns are addressed, organizations can easily deploy NLP in their operating system.