Applications of Machine Learning and Artificial Intelligence to Real-World-Business

Ever-increasing computing power and the development of effective prediction algorithms has caused a surge in Machine Learning applications within the last decade. Closely related and usually considered as a sub-field of Artificial Intelligence (AI), Machine Learning is the process of automatic detection of usable patterns within data. Such algorithms have proven to be extremely useful for a wide variety of different tasks, including facial recognition, speech recognition, and predictive maintenance. However, the current hype around Artificial Intelligence and Machine Learning has failed to deliver the countless real-life use-cases that were promised up to this point. Still, there are some real-world machine learning applications that are being used today. This article will provide an overview of how Machine Learning – and Artificial Intelligence in general – has the potential of drastically revolutionizing different fields of the business environment, and how it will bridge the gap between current business applications and Industry 4.0.

Machine Learning for Natural Language Processing and Natural Language Understanding

Within the scope of peer-to-peer communication, natural languages are often referred to as the languages that humans use to interact with each other. As the field of Machine Learning and Artificial Intelligence progressed, natural language processing has become increasingly more important within the ever-evolving business environment. Current NLP systems are able to analyze and process different forms of human language, including written text and audio data. These technologies are starting to find their way to the business environment as well, where they can be used for automatically reading (or transcription) hand-written sentences, classifying incoming e-mail traffic of perform in depth data mining on conversational recordings from customer calls.

Machine Learning and Natural Language Processing for e-mail classification

Large quantities of incoming e-mail traffic make it difficult for businesses to effectively and efficiently help people with their customer support, order inquiries, or meeting scheduling. This because – usually – incoming e-mail traffic needs to be manually reviewed by personnel before forwarding it to qualified employees which are able to process the e-mail’s content.

However, natural language processing and machine learning techniques have allowed the classification of incoming e-mails, ruling out the task of manually identifying the context and problem within each e-mail. Such algorithms are able to create queues of e-mails which belong to a specific category, after which qualified support personnel can select their queue of expertise. In this way, businesses are able operate more efficiently by reducing response times, reduce labor costs, and increase customer retention by providing exceptional customer support.

Speech recognition for customer Intelligence

Nowadays, powerful speech recognition software is able to perform in-depth data-mining on the audio files obtained from customer calls. Such data-analysis may provide key demographic information about the caller such as gender, age, accents, emotion and sentiment. This valuable information allows businesses to gain powerful insights regarding their customer base, launch highly targeted marketing campaigns and improve support and sales performance.

Another application of speech recognition within a business context is the use of automatic text transcription software. This software allows to convert audio and video fragments into perfectly accurate text documents which contain the spoken sentences from the imported audio file. This may be useful to acquire transcriptions of board meetings, conference calls or shareholder’s meetings in order to easily transfer this information to people which were not able to attend the event.

Energy forecasting using time-series prediction

A Recurrent Neural Network is a type of machine learning method with memory-like elements in its architecture, making them especially suited for dealing with sequential input data. This makes them especially suited for the purpose of time-series forecasting: the branch within machine learning that focusses on predicting parameter values in the future, based on the parameter values that have been observed in previous timepoints.

Time-series prediction for sales forecasting

Many production environments are limited by the amount of stock that can be kept within the company walls, requiring them to carefully consider their production output and predict future product demand. Recurrent neural networks and time-series prediction provide businesses with an analytical tool to perform such predictions, enabling them to enhance their sales forecasting and reduce product stock. In addition, such precise sales forecasting tools allow businesses to better gauge required workforces, implement a clear recruitment strategy, and eliminate temporary lay offs.

Time-series prediction for energy forecasting

Using data on the market’s energy demand during the recent years, recurrent neural networks can be trained in order to provide an estimation about the market’s energy demand in future time-points. Since a perfect balance between energy supply and energy demand should always be maintained, having knowledge about the near-future energy demand allows providers to either increase or decrease production accordingly. This allows energy providers to obtain increased production efficiencies because of the reduced risk of over-production and better insights into general market trends.

Digital Twins Technology to Aid Model-Based System Engineering

In recent years, engineering environments have become increasingly more complex due to strict safety regulations and higher efficiency demands. In such complex environments, the use of regular Computer Aided Engineering (CAE) systems have become cumbersome and inefficient, requiring the need for more modern design techniques. Therefore, innovations such as Digital Twin technology, Machine Learning, and Artificial Intelligence are becoming increasingly more relevant to industry environments by aiding in both Model-Based System Engineering (MBSE) and monitoring machine operations.

Digital twin technology – like traditional CAD-based systems – represent a 3-dimensional virtual model of a physical systems such as industrial machines (e.g., gas turbines, mechanical drives or gas compressors). However, digital twin technology allows the real-time monitoring of individual machines or production environments by linking the virtual model with IoT sensory data from the physical environment. In this way, Digital twin technology poses some serious advantages in comparison to traditional CAD-based models:

  • Every industrial system operates under different circumstances and within different environments. Using Digital Twins, real-time operation monitoring allows one to obtain a digital model of a specific machine, aiding in the scheduling of individual machine maintenance and the projection of future system performance on an individual level.
  • Digital Twin technology allows R&D teams, designers, and engineers to observe system behavior within different operational environments. This stream of information enables them to get a deep understanding about the performance of specific design modifications in certain ambient conditions and possibly make relevant adjustments with reference to this operational environment.
  • The Digital Twin’s system model is updated in real-time during the operation of the physical model. This allows industrial manufacturers to accurately trace the machine’s life cycle and to get a clear understanding of the physical system’s age and possible future malfunctioning.

In addition, this physical-digital linkage allows manufacturers to run simulations on Digital Twins, based on data obtained from physical systems that are already operational. In this way, Digital Twins can be provided with the following information during simulation:

  • Individual service and maintenance history
  • Operation history and environmental data capture by IoT devices
  • Product design history
  • Machine configuration during operation

Simulation results can then be compared to real-world outcomes (from the operational physical system) which – along with the CAD design files – can be analyzed by machine learning models to provide valuable insights about the physical twin and its possible design improvements and maintenance requirements within their specific production environments.

Conclusion

Increasingly more applications – powered by Artificial Intelligence and Machine Learning – are making their way to the business environment, allowing the automation of all industry aspects as the technology advances. In addition, advanced machine learning techniques like deep learning, convolutional neural networks, and recurrent neural networks are paving the way to human-like intelligence for many real-life problems. It is exciting to see what Artificial Intelligence has in store for us and how it will further improve all aspects of the business environment.

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