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Employment of NLP within Manufacturing

Employment of NLP within Manufacturing

Natural Language Processing (NLP) is a subset of Artificial Intelligence that helps identifying key elements from human instructions, extract relevant information and process them in a manner that machines can understand.

Integrating NLP technologies into the system helps machines understand human language and mimic human behaviour. For example, Amazon's Echo, Microsoft's Cortana and Apple's Siri make extensive use of NLP technologies to interact with the users.

NLP technologies can help in the interaction with the machines and speed up the operation of different types of manufacturing systems, cutting down the response time. Imagine a scenario where a manufacturing company hires a data scientist to collect shopfloor worker information and analyse all the machine readings, reporting any sort of problems. One disadvantage to this scheme is that by the time the management reads the report one problem might have happened causing damage to the entire process. If a computer or robot with sensors and NLP technologies embedded is employed, this might analyse information coming from the machines, reports from customers and information from workers, in order to obtain relevant information about the process. This computer or robot might even communicate with users and accept input in natural language.

Within the manufacturing industry, the NLP might be adopted for example for the following tasks:

  • Process Automation: The use of NLP technologies in the manufacturing process allows the automatic processing of information in natural language and the execution of repetitive tasks like paperwork and report analysis.
  • Inventory Management:  Analysing data about the stock, sales and user reports of certain products is essential to assess the correct decisions for a company to optimise and maximise profits. By leveraging NLP technologies the resulting benefits are: 1) the entire process becomes more comprehensive; 2) it is more difficult to incur errors related to the analysis of sales; 3) it is easier to analyse the manufactured products and discard those with low quality without affecting the supply chain and sales.
  • Emotional Mapping: Sentiment analysis and emotion detection are one of the most exciting features of NLP. Early NLP systems allowed organisations to collect speech-to-text communication without accurately determining its full meaning. Today, NLP approaches can sort and understand the nuances and emotions in human voices and text, giving organisations unparalleled insight. Learning customer expectations and operators' viewpoints is a very important element in manufacturing. NLP technologies permit to identify emotions and the polarity of the opinions of customers and operators and provide actions to improve products and different processes. For example, knowing the expectations of customers is key to building a longer relationship and creating engagement with them.
  • Operation Optimisation: Furthermore, NLP technologies can be employed to trace the performance of equipment and improve the interaction with machines. This simplifies the operation of complex systems and can enable Human Machine Interaction where the operator and the machine collaborate in order to optimise processes.


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Therefore, by leveraging NLP technologies, both the decision makers and operators can improve the collaboration between humans and machines within the manufacturing sector and increase the knowledge about their systems and processes.

STAR project aims to enable the deployment of secure, safe, reliable and trusted human centric AI systems in manufacturing environments. Many of these AI systems require interaction with humans and machines and can often benefit from NLP techniques. For example, Speech-to-Text and Text-to-Speech capabilities can enable multimodal interaction with the system, or sentiment analysis can evaluate the polarity of the messages the system receives and adapt to the user's mood. These user-centric ideas are within the NLP activities of STAR.

By: Diego Reforgiato Recupero, Nino Cauli and Rubén Alonso / R2M Solution and University of Cagliari