Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.

Sustainability is currently one of the main issues in all media and in society as a whole and is increasingly discussed in science from different sides and areas. Especially for the automotive industry, sustainability becomes more and more important due to corporate scandals in the past and topics such as electric motors, lightweight construction and CO2 emission reduction are key issues. Although the focus is primarily on other components, the interior cannot be neglected either in terms of sustainability. Interior is the most frequently seen part of the car by the driver. Therefore, e.g. the use of natural fibres especially for premium brands can only be considered in connection with highest standards regarding practical and aesthetical aspects. Consequently, the following research question arises: How do the three pillars of sustainability (economical, ecological and social issues) influence interior development at premium brand manufacturers and how do customers accept sustainable solutions? The focus of the paper is exclusively on premium brands due to the higher spread of sustainability effects compared to volume brands. A quantitative study is carried out to determine the expectations on the customer side regarding more sustainability in the automotive industry in general and in the interior sector in particular and to derive corresponding challenges and potentials for original equipment manufacturers.

In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber–physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber–physical environment.

Background and Purpose: In a digitally globalized world cross-cultural interaction in social and business environment has become more widespread than ever before. The purpose of this paper is to explore the attitude of Generation Z representatives to different aspects of cross-cultural interaction. Design/Methodology/Approach: We used an online questionnaire to collect data for our study. A sample of 324 young adults from three countries: Germany (n=113/34.9%), Romania (n=107/33.0%) and Ukraine (n=104/32.1%) participated in the online survey. The sample consists of university students aged less than 23 years to match the criteria of being representatives of Generation Z. Different statistical tools were used to check the hypotheses. Results: The results of the study indicate that Generation Z representatives consider cross-cultural communication skills as highly important both in their private and business life. The main motivation factors to work in a multicultural business environment have been identified as well as major barriers for effective cross-cultural interaction. Conclusion: This paper illustrates that Gen Zers are willing to work in a multicultural business environment; moreover it can give them additional motivation. This trend along with ongoing processes of globalization and digitalization fosters further interconnection of countries and regions of the world, making cross-cultural communication and cross-cultural management techniques even more important.

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