Artificial intelligence (AI) is becoming increasingly important in our daily lives, but especially in industrial companies. Smart product development, predictive maintenance and self-organizing logistics are just three of the many areas in which industrial AI can be found today. This article shows where artificial intelligence is already being used in Industry 4.0 and how it works.
There is no uniform, generally accepted definition of the term artificial intelligence (AI). In general, artificial intelligence refers to a field of computer science that deals with computer technologies that are capable of solving intelligent tasks. Thus, AI extends technical systems by the ability to process tasks independently.
For example, computer systems that process tasks based on the analysis of large amounts of data are referred to as AI. They then make decisions based on the data derived from the data obtained. The quality of the actions depends crucially on the knowledge of the AI system, i.e. the quantity and quality of the collected data.
During operation, AI systems are able to continue learning on their own thanks to "learning" algorithms. Over time, they expand their knowledge base and optimize their trained models.
Current approaches in research and development around artificial intelligence in industry are human-centric. This means that the focus is on using AI to best support support people in their activities in the best possible way. The reason: Artificial intelligence enables rational human actions and thinking – i.e. learning, planning and solving problems – to be implemented.
This includes the following processes:
However, AI is not about copying human behavior. Instead, the aim is to use artificial intelligence in industry in such a way that industrial processes become more efficient and effective. The main goals of artificial intelligence in industry are cost reductions, time savings and improvements in product quality. Basically, the aim is always to increase the rationality of industrial processes.
Additionally: Artificial intelligence in industry can also be used to completely redesign production processes and introduce new types of business models. For example, many start-ups are making AI-based solutions their business model.
Artificial intelligence is part of Industry 4.0 and can be seen as one of its centerpieces. Industry 4.0 is based on the trend of networking production resources and components with each other. AI represents a kind of aid in this development, because it helps to effectively process the data obtained and creates intelligent connections between the various processes. For this, however, the applications require not only large quantities of data (Big Data), but also the right data (Smart Data).
A representative survey of German industrial companies in 2019 revealed that more than one in ten companies uses artificial intelligence in industry.[1] Compared to previous years, this result can be seen as a success. However, the fact that the number of companies is still relatively low is due to the fact that the implementation of AI applications in the corporate structure is associated with numerous changes and efforts.
In medium-sized and large companies, AI applications are found primarily in the area of robotics and resource management. Here, for example, robots that perform tasks independently and AI systems that guide human workers during repairs are used.
One form or variety of AI is machine learning, which involves training the AI system or software. The system draws conclusions from patterns or recurring conditions and improves future production processes in this way. Artificial intelligence in industry also makes it possible to intelligently control complete production plants. Artificial intelligence in industry can, for example, enable autonomous (driverless) transport vehicles to take on tasks independently.
Machine learning helps equip robotic systems with the capabilities needed for flexible production. The applications must be able to interpret data, find patterns and correlations, and draw logical conclusions.
Algorithms that receive the data collected by the machines via software or cloud applications help identify patterns and anomalies. From the volume of data, they then obtain information about the production process, for example, the specific sequences in production, upcoming maintenance or idle times.
Using the data, the machines develop a predictive maintenance service, for example. They are able to detect potential faults such as missing industrial spare parts at an early stage and rectify them – before they can cause any damage.
Machine learning has the advantage of coordinating all production processes as efficiently as possible. In view of the fact that even short disruptions often have enormous economic consequences, machine learning offers the opportunity to optimize productivity and availability. In addition, there is optimization potential in terms of cycle time, process quality, energy consumption and maintenance intervals.
But how intelligent is artificial intelligence in industry? What is the real state of the learning ability of robots? Artificial systems are far from simply learning knowledge or practical examples by heart. Instead, they learn in practice and recognize patterns and correlations or regularities in the process. They are also able to generalize the knowledge gained and apply it to other cases.
To achieve this capability, artificial intelligences in industry rely on information from data that they recognize and sort. Artificial intelligence in industry can be based on programmed processes or on machine learning.
Using so-called deep learning as a machine learning method, artificial intelligences learn based on artificial neural networks. These are data and computing networks that have an extensive structure and are modeled on the human brain. Ultimately, the system trains itself to be able to derive regularities from data and uses them to make future decisions.
The use of artificial intelligence in industry is so valuable because the corresponding tech and software applications lead to more efficient production methods. In addition to AI, other technologies – such as Internet of Things, Blockchain, or Quantum Computing – play a role in the various development scenarios in production and development.
Artificial intelligence always plays a key role in this. A study by the Berlin Institute for Innovation and Technology, for example, shows that AI is enormously important for value creation in the manufacturing industry. Currently, artificial intelligence is mainly applied in industry for the automation of repetitive production processes as well as for supply chain and warehouse management.
Machine learning methods (e.g., neural networks) are used to train artificial intelligences in industry to make decisions over and over again. To do this, they need sufficient training data – because the greater the quantity of data and the higher the data quality, the better the training and the predictions of the system. Artificial intelligence in industry is already being used in various fields of manufacturing, especially in the context of automation technology.
[1] https://www.bitkom.org/Bitkom/Publikationen/Bitkom-Mittelstandsbericht-2019
