The Next Level of Intelligent and Self-Optimizing Factories

Industry 4.0+: The Next Level of Intelligent and Self-optimizing Factories

Erwin Rauch(&)

Free University of Bolzano, 1, Universitätsplatz, 39100 Bolzano, Italy [email protected]

Abstract. For almost a decade now, production science has been dealing with Industry 4.0. In recent years, a large number of technological innovations have been developed and introduced into practice, enabling the implementation of smart and connected manufacturing systems. Over the next years, researchers and practitioners will face new challenges in Industry 4.0 to achieve the original vision of an intelligent and self-optimizing factory. We are currently at a crossroads between the ﬁrst level of Industry 4.0, which was characterized by technologically driven innovations, and a future level of Industry 4.0+, which will be based on data-driven innovation. This article introduces these two phases of Industry 4.0 and gives a direction of research trends with growing attention in manufacturing science and practice. In the context of Industry 4.0+, two research directions, in particular, are expected to generate groundbreaking changes in production and its environment. This is, on the one hand, the introduction of Artiﬁcial Intelligence into manufacturing and on the other hand the use of nature as inspiration in the form of Biological Transformation.

Keywords: Industry 4.0 Á Industry 5.0 Á Society 5.0 Á Intelligent manufacturing Á Self-optimization Á Artiﬁcial intelligence Á Biological transformation

1 Introduction

Digital technologies are increasingly changing society. In the production sector, the term ‘Industry 4.0’ (I4.0) in particular introduced a new era of digitally networked production almost 10 years ago. The basic idea of Industry 4.0 was to be able to unfold the advantages through comprehensive connectivity on the shop floor as well as, by connecting products, machines, employees with the production system and with all those involved in the value chain, thereby minimizing information disruptions and the resulting inefficiencies by smart factories. To manage interconnected systems between physical assets and computational capabilities so-called cyber-physical systems (CPS) were introduced as transformative technologies leveraging the interconnectivity of machines. Since the proclamation of Industry 4.0 in 2011 at the Hannover Fair [1], a lot has happened in this direction. While Industry 4.0 was mainly limited to Germany in the first few years, almost all European countries have now launched Industry 4.0 initiatives. A look at scientific databases such as Scopus shows that since 2017 a large number of international publications have been added. Most of the larger companies

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 V. Ivanov et al. (Eds.): DSMIE 2020, LNME, pp. 176–186, 2020. https://doi.org/10.1007/978-3-030-50794-7_18 Industry 4.0+ 177 have already started initiatives and pilot projects to introduce new technologies related to Industry 4.0 in production as well as logistics. Many of the small and medium-sized companies (SME) do not yet have such a smart and connected manufacturing system, but will be able to achieve this goal in the medium-term as results from research are already transferred into broader industrial practice [2]. The next groundbreaking level to be achieved, are intelligent and self-optimizing manufacturing systems. While a smart factory can be understood as a manufacturing system, which is capable to apply previously acquired knowledge an intelligent factory may be seen as a factory, which can autonomously acquire new knowledge and apply it for self-optimization purposes. To achieve this goal the results from the first era of Industry 4.0 play, an important role as connectivity and modern technologies are a prerequisite for the next level of Industry 4.0 called ‘Industry 4.0+’ in this article. Currently, several authors are speaking also about Industry 5.0 [3, 4] although it might be seen more like the second level of Industry 4.0 with a final vision of an intelligent and self-learning and self-optimizing factory. Based on the results of the first level, production resources can collect a large amount of high-quality data using sensors, vertical and horizontal data integration guarantees seamless data exchanges, a large amount of big data can be stored and managed via cloud technologies and be processed into more structured data with big data technologies. The next level of Industry 4.0+ is aimed at taking advantage of this data creating intelligent and self-optimizing factories, whereby we are already still far away from this vision. It is important to look for new and innovative solutions to how this new level of data quantity and quality can be utilized in companies for self-monitoring and intelligent self-optimization of the manufacturing system. Artificial intelligence (AI) and biological transformation in manufacturing may open up completely new possibilities in this direction. Although the theoretical basis of AI and approaches of bio-inspired manufacturing existed already years ago, now is the right time to take full advantage of these concepts as large amounts of data are available in factories and computational capabilities increased significantly in the last years. This article introduces the concept of implementing Industry 4.0 on two levels, a first technology-driven level, and a second data-driven level. In this visionary look in the future, the author gives an outlook on how to achieve the vision of intelligent and self-optimizing factories of the future with Industry 4.0+. To reach this vision researchers and practitioners will need to deal with artificial intelligence and the concept of biological transformation, which will most probably dominate research in manufacturing in the next decade.

2 Literature Review

2.1 Industry 4.0 – The Fourth Industrial Revolution Industry 4.0 is the umbrella term for the Fourth Industrial Revolution, which has particularly occupied scientists in production engineering over the past almost 10 years. The term was presented for the first time at the Hannover Messe 2011 by German scientists (Acatech - National Academy of Science and Engineering), who wanted to 178 E. Rauch sensitize the public and policymaker to a new high-tech strategy for Germany. In the following two years, a working group was set up in Germany to develop recommendations for the implementation of Industry 4.0. In 2013, the final report “Recom- mendations for implementing the strategic initiative INDUSTRIE 4.0” was presented to the public [1]. In the following years, mainly from 2014 to 2016, most of the European countries launched national initiatives and funding programs to roll out Industry 4.0. To name just a few, these are the “Piano Nazionale di Industria 4.0” in Italy [5], “Smart Industry - a strategy for new industrialization for Sweden” in Sweden [6], “Industrie 4.0 Österreich” in Austria [7]or“Industria Conectada 4.0” in Spain [8]. According to a keyword search for “Industry 4.0” (selecting only non-European countries) mainly since 2017, Industry 4.0 has also achieved significant status as a term on an international level. Especially in Asia, the term Industry 4.0 is widespread. Thailand with the initiative “Thailand 4.0” [9]or“Made in India” [10] can be mentioned here as an example. In the North American region (USA and Canada) the concepts of Industry 4.0 are often known under the terms “Internet of Things” (IoT), Smart Manufacturing or Intelligent Manufacturing [11]. If we look back to the beginnings of Industry 4.0, what were the challenging goals for this new Fourth Industrial Revolution back then? The final report of Kagermann et al. [1] may be used as one of the first documents on Industry 4.0. Industry 4.0 is the Fourth Industrial Revolution after three previous revolutions. After mechanization at the end of the 18th century (1st Industrial Revolution) and electrification at the beginning of the 20th century (2nd Industrial Revolution), computer technology, electronics, and automation were introduced at the beginning of the 1970s (3rd Industrial Revolution). The Fourth Industrial Revolution is characterized by the aim to connect machines, people, products, and the entire value network by vertical and horizontal data integration to create smart and connected factories. According to [1] “smart factories constitute a key feature of Industry 4.0 being capable of managing complexity, being less prone to disruption and able to manufacture goods more efficiently”. In the widest vision of Industry 4.0, smart factories become intelligent factories. They will lead to the emergence of dynamic, real-time optimized, self-organizing manufacturing systems with production facilities that are autonomous, capable of controlling themselves in response to different situations, self-configuring, self- regulating, self-aware and self-optimizing [1, 12]. In such intelligent factories, employees will be freed up from having to perform routine tasks, enabling them to focus on creative, value-added activities. They will thus retain a key role, particularly in terms of supervision and quality assurance [1]. A smart factory enables rapid and flexible adaptation or reconfigurability through connected machines able to get data as well as to offer information to other elements in the manufacturing system (e.g. people, products). Intelligent factories can think, learn, remember and in a given moment share that amount of knowledge, or react in certain situations [13]. Intelligent manufacturing systems are highly automated at the manufacturing level and are self-repairing, self- optimizing and self-configuring by taking advantage of AI and neural networks technology [14]. Industry 4.0+ 179

2.2 Industry 5.0 – Is this the Next Industrial Revolution? After about 10 years of industry 4.0 and ever shorter innovation cycles in a highly dynamic environment, many scientists are naturally asking and looking for the next big hype in production science. For this reason, in some studies, a Fifth Industrial Revo- lution has already been heralded (Industry 5.0) while other works and national programs introduce the term Society 5.0. In the following, we will have a more detailed look at these terms. Looking for the keyword “Industry 5.0” the first listed work in Scopus from Sachsenmeier has been published in 2016 and introduces Bionics (the imitation or abstraction of the “inventions of nature) as the next disruptive revolution in the industry [15]. Other works like Özdemir and Hekim published in 2018 [3] or Pathak et al. in 2019 [16] bring in AI and therefore intelligent cyber-physical systems as the next game changer in the industrial field. The fifth revolution is described in [16] as a cyber- physical system comprising people, AI and the physical system of enterprises well connected through high-speed internet and in particular the application of collaborative robots (cobots) in manufacturing. Also in [4] Industry 5.0 is explained as the concept, where robots are intertwined with the human brain and work as a collaborator instead of a competitor. In [17] the authors address also fast decision-making processes as well as a mass customize based platform collaboration to be part of a new industrial environment where firms are involving their customers more closely. If we now evaluate all these definitions of Industry 5.0 as a new industrial revolution, it becomes relatively quickly clear, that these are topics and goals which were already discussed years before in Industry 4.0 and which, due to the latest progress (e.g. in AI), are moving within reach. To answer the question posed in the title of this section: no, these issues do not in any way herald a new groundbreaking and all-changing industrial revolution.

2.3 Society 5.0 – An Extension of Industry 4.0 or a New Revolution? An interesting development in this regard is the rise of the term ‘Society 5.0’ in Japan. The Society 5.0 (SuperSmart Society) was introduced in 2016 by Japan’s most important business federation (Keidanren) and being strongly promoted by Council for Science, Technology, and Innovation; Cabinet Ofﬁce, Government of Japan [18, 19]. Society 5.0 is not limited only to manufacturing, but it solves social problems with the help of advanced IT technologies, IoT, robots, AI and augmented reality (AR). These technologies are actively used in people’s common life, at work, in healthcare and other spheres of activity for the beneﬁt and convenience of every single person [20]. In [21] and [22] the different social (and not industrial) revolutions are explained as follows. • Society 1.0 means hunting and gathering, • Society 2.0 is the agricultural society, • Society 3.0 is the industrial society, • Society 4.0 is the information society, • Society 5.0 is the ‘super smart society’. 180 E. Rauch

This leads us to become more familiar with the concept of the Industrial Revolu- tion. How is an industrial revolution characterized and what elements make a period an industrial revolution? According to [1] an industrial revolution brings a radical transformation of the world in which we live and work. In the last industrial revolutions, the radical transformation was mainly characterized by a change from “manual labor” towards “brainwork” and therefore a signiﬁcant impact on industrial work and our social life. Following [23] and as stated by Joseph Schumpeter such revolutions can be seen as “create destruction” in which old industries died and new ones were born. Very often so-called disruptive technologies accelerated or extended such kind of revolution [23]. Friedrich Engels in ‘The Condition of the Working Class in England’ [24] in 1844 spoke of “an industrial revolution, a revolution which at the same time changed the whole of civil society”. Therefore what is currently understood by the term Society 5.0 is nothing completely new compared to Industry 4.0, but can be seen as a more far- reaching extension to people’s life, as it envisions a complete change in our society and population.

3 Research Methodology

In this research, we analyze the focus of the last years of research on Industry 4.0 as well as future trends and main topics to be addressed in the next years. Thus, we introduce the concept of a two-step implementation of Industry 4.0. Figure 1 shows the classic picture of the four industrial revolutions with the extension of Industry 4.0 by the next level of Industry 4.0+. The ﬁrst level aims at achieving a smart and connected factory, while the next and future level of Industry 4.0 aims to achieve an intelligent and self-optimizing factory. The ﬁrst level of Industry 4.0 is characterized by technology-driven innovation, which is also the prerequisite for the next level. The second level of Industry 4.0 is characterized by data and intelligence-driven innovation.

Data and Intelligence-driven Technology-driven InnovaƟon InnovaƟon Industry 4.0+

Industry 4.0 ArƟﬁcial Intelligence and biological DigitalizaƟon, IoT and transformaƟon 3rd Industrial advanced technologies Intelligent and Self- Smart and OpƟmizing Factory RevoluƟon Connected Factory

IntroducƟon of IT, 2nd Industrial electronics, computers AutomaƟon RevoluƟon

IntroducƟon of electrical energy Complexity 1st Industrial ElectriﬁcaƟon RevoluƟon

IntroducƟon of mechanical power MechanizaƟon

Late 18th century Beginning 20th century Early 1970s 2011 future

Fig. 1. Industry 4.0+: intelligent and self-optimizing factories as the next level in manufacturing. Industry 4.0+ 181

4 Results

4.1 The First Level of Industry 4.0: Technology-Driven Innovation In recent years, the main goal has been to create smart and connected factories that can also collect and process in real-time large amounts of data related to the product and manufacturing system (also known as ‘Digital Shadow’)toefficiently produce per- sonalized and mass customized products (with the target ‘lot size 1’). This data can be used to create a digital twin as a prerequisite for a cyber-physical production system (CPPS) [25]. Various works are dealing with technologies of Industry 4.0 [26]. In recent years the categorization into the nine key technologies [27] shown in Table 1 has established itself and is used as a basis for many national Industry 4.0 programs. Table 1 shows the fundamental benefits that these technologies enable. The first level of Industry 4.0 was mainly technology-driven to create the prereq- uisites for the next level of Industry 4.0. All these technologies have fundamentally contributed to the creation, storage, protection, exchange, processing, “simple” analysis and visualization of information or data, as well as to give people in the manufacturing system the opportunity to interact with the virtual world.

Table 1. The main benefit of Industry 4.0 technologies [27] in the first level of Industry 4.0. No. I4.0 technology Description Main benefit 1 Autonomous Collaborative robots that are easy to Human-machine robots program and interconnected with the collaboration manufacturing system 2 Additive Produce small batch sizes of complex Produce manufacturing customized products on demand in customized geographically decentralized products on manufacturing units demand 3 Virtual and Provide workers with real-time Visualization of Augmented information to improve their work or data and interaction Reality (VR/AR) decision making as well as for virtual training or interaction 4 Simulation Simulation systems to mirror in real-time Processing of data the physical world in a virtual model for and interaction testing, optimization, and interaction 5 Horizontal and Data integration along the value chain Exchange and vertical data (horizontal) and from the enterprise level visualization of integration to the machine level (vertical) data 6 Industrial Integration of field devices and sensors Gathering, Internet of for increasing multidirectional exchange, and Things communication, interoperability, and visualization of decentralized control data (continued) 182 E. Rauch

Table 1. (continued) No. I4.0 technology Description Main beneﬁt 7 Cloud Storage and management of a large Storage and amount of data on open sharing management of platforms to achieve short reaction times data and thus enabling data-driven services 8 Cybersecurity Protect manufacturing systems from Protection of data cyber-attacks through secure, reliable communications as well as sophisticated identity and access management 9 Big data and Collection and comprehensive “Simple” analytics evaluation of production and enterprise processing and data analysis of data

4.2 The Second Level of Industry 4.0: Data and Intelligence Driven Innovation While in the first level of Industry 4.0 we were mainly concerned with the technologies with which we could generate a higher quality of data and handle larger amounts of data, the second level of Industry 4.0 will be far more data and intelligence-driven. In the coming years, the goal will be to fully realize the Industry 4.0 vision by equipping our manufacturing systems with intelligence using nature as an inspiration and profit from the latest advances in AI. This will make it possible to create future intelligent and self-optimizing factories, as announced in the first vision of Industry 4.0 [1]. Artificial Intelligence in Manufacturing. AI is currently on everyone’s lips and is expected to dominate production research over the next years. We can differentiate in AI the following three terms: artificial intelligence, machine learning (ML) and deep learning (DL). In general deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence [28]. AI is a branch of computer science and defined to be the science and engineering of making intelligent machines. In AI a computer system can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision- making, and translation between languages [29]. ML as a subset of AI is empowering computer systems with the ability to “learn”. ML intends to enable machines to learn by themselves using the provided data and make accurate predictions. DL is a subset of machine learning and the next evolution of ML. DL algorithms are roughly inspired by the information processing patterns found in the human brain. It refers to the number of layers in a neural network making it able to process also unstructured data compared to machine learning techniques where features for classification need to be provided manually [28, 29]. Industry 4.0+ 183

An intelligent and self-optimizing manufacturing system can be realized by using AI (including ML and with increasing amount and complexity of data especially DL). Possibilities for the application of AI in manufacturing are expected in automated or assisted engineering design, manufacturing system reconfiguration, production plan- ning, predictive maintenance, quality inspection as well as in supply chain management [30]. The introduction of AI in manufacturing enables manufacturing systems to become self-aware, self-comparing, self-predicting, self-optimizing and thus also more resilient as traditional manufacturing systems [12]. Biological Transformation – Nature as Inspiration. Resilience is also one of the central characteristics of many biological systems. From a biological point of view, resilience is a property that enables a system to maintain its functions against internal and external disturbances [31]. Increasing technical capabilities in information processing and computer capacities have enabled a growing understanding of biological processes in our environment in recent years. It is to be expected that biology and information technology will grow closer together in the future. Therefore, biological transformation is also seen as a parallel process to digital transformation [32]. According to [33], biological transformation can be transferred in three levels to industrial production: • Bio-inspired manufacturing: involves the imitation or transfer of phenomena from nature to complex technical problems. • Bio-integrated manufacturing means the integration of technological and biological processes into industrial value-added processes. • Bio-intelligent manufacturing: as the combination of technical, informatics and biological systems creating robust and self-sufficient value creation systems. This results in completely new potentials in the use of nature as a source of inspiration by not only imitating biological effects but by intelligently transferring principles from nature to various fields of application, such as manufacturing. This can be seen as a process that interacts symbiotically with digital transformation. While the first two levels of biological transformation mentioned above have already been applied in the past and present, the third level represents a groundbreaking innovation that will be able to fully unfold its full potential shortly based on the latest Industry 4.0 technologies and enhanced by the progress in AI.

5 Conclusions

This article shows that Industry 4.0 can be structured on two levels. The first level of Industry 4.0 was determined by technology-driven innovations, which serve as pre- requisites and enablers for the creation, management, and processing of data. The next years will be characterized by the next data and intelligence-driven level of Industry 4.0 (Industry 4.0+) to achieve the vision of an intelligent and self-optimizing factory of the future. The paper shows that mainly two future directions will dominate research in this 184 E. Rauch area: (i) AI to transform data into autonomous intelligence and biological transformation to learn from nature in how to deal with complex problems. In addition, the term Industry 4.0+ is discussed concerning other terms such as Industry 5.0 and Society 5.0. The detailed analysis shows that the current Industry 5.0 definitions are only the next level of Industry 4.0, which is referred to as Industry 4.0+ in this article. Con- cerning the term Society 5.0, it was shown that this term is based on other social revolutions and is merely an extension of Industry 4.0 to social life. A visionary outlook into the research landscape of the coming years is given to stimulate further research activities in AI and biological transformation. Further, the implementation of technologies from both the first phase and the second phase of Industry 4.0 requires the development and consolidation of appropriate standards.

Acknowledgment. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 734713 (SME 4.0 – Industry 4.0 for SMEs).

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