If one can believe the answer of Amy Webb, founder of the Future Today Institute, to the question "What is the next big tech-thing", "(...) artificial intelligence (is) the most important technical development. It heralds the third era of computer science. AI will be everywhere in the future." [46].
The Chinese government, for example, is investing hundreds of billions of dollars by 2030 in topics such as artificial intelligence and deep learning to improve its progress over other countries [7].
On the other hand, the European Union would like to invest a "(...) sum of 1.5 billion euros (by 2020), which seems almost petty in comparison." [14].
A current statistic from Statista shows that the turnover generated by artificial intelligence will increase by up to twelve times the current value by 2025 and that the largest shares will be distributed over North America and Asia.
But are topics such as artificial intelligence and the associated neuronal networks really new? According to Sigmar Gabriel and his published article in WirtschaftsWoche it is "(...) an old field of research in which research has been carried out since the 1950s" [14] and also according to Google trends [16] the topic of artificial intelligence has been a sought-after topic since 2004 and arouses interest among many people.
After an initial steady decline in search queries, interest in the AI rose sharply again from mid-2016 onwards due to rapidly increasing computing power, so that it is currently on everyone’s lips.
However, the topics of machine learning and big data only became interesting for the general public at the beginning of 2012 and usually form the cornerstones of artificial intelligence.
"Intelligence is the ability to adapt to change.", Stephen Hawking said at a press conference. Currently, numerous startups and large American corporations are showing what artificial intelligence can do.
Local companies must not oversleep this growing trend or this change in the development of products by putting risks before new opportunities or the newly emerging business areas.
According to Sigmar Gabriel, however, German companies should "(...) develop new products and processes that (improve) what already exists" and not "(...) follow the business models of Google, Amazon and Co." [14].
And according to a McKinsey study conducted in 2017, the use of intelligent robots and self-learning computers could increase Germany’s economic output by 160 billion euros compared to today, and not necessarily with fewer employees [25].
However, an IDC study from June 2017 states that around 40 percent of the companies surveyed in Europe and the USA plan to actively benefit from AI technologies by the end of 2018 and use them [21].
Amy Webb says that "the consequences (if a company does not react to artificial intelligence) can be devastating (...)" and that "(...) any commercial sector will be affected (...)" if more and more companies take a closer look at the topic [46].
In order not to be left behind in this competition, more and more manufacturing companies from Germany are dealing with topics such as artificial intelligence and neural networks in order to equip their products with intelligence so that they become better or faster.
Robert Bosch GmbH, with its subsidiary Packaging Technology GmbH, also has a strong interest in making the packaging machines it produces more efficient or in making it easier to convert them to new products.
The root cause analysis by Don Norman from his own book The Design of Everyday Things [32] states that the cause of human action to solve problems occurs by answering the five why questions. All other problems are then solved by a chain reaction.
Five answers to the question as to why this or that should be done lead to the conclusion that the core problem actually needs to be solved.
Derived from this, this work, with the help of artificial intelligence and a neural network, aims to find a way to eliminate long-lasting configurations of machines or unnecessary test runs.
This should make it possible to make the packaging machines produced work more efficiently and to convert them more easily to new products.