Cloud computing: Also identified as one of the most essential criteria when it comes to I4.0 the measurement is critical. The cloud computing can be structure in external and internal cloud computing as well as in what type of service is provided by the cloud. These types are Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Further in it needs to be distinguished between service provider and service consumer. The base measurements of performance are the same. Overall response times, business logic calculation times, transaction processing times and availability of the service. The closer a company reaches 100 availability when using or providing a service the higher their maturity rating. IOT: When it comes to IOT many different components are to consider. From smart products like cars, phones, over smart sensor, RFID tags to smart machine the range of smart devices is big. The first stage of IOT maturity is hence simple device connectivity and data forwarding. The second stage is then the possibility of real time monitoring. The third stage is data analytics followed by the fourth stage of automation. The fourth stage we consider as the point where the introduction of smart products makes most sense. Especially as all machines are connected and the analytics are already mature. Until this point we considered the process more as a cyber physical system. But after stage 4 one can talk about the IOT. This would also imply that from this stage onwards smart products would not only deliver during the production but also after they have been distributed to the customer. The last stage then is the on board intelligence. Meaning that every machine and every connected device has its own data analysis function.
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The Mitsubishi Electric e-factory solution comes into play to meet these expectations and ensures that production and management departments of a factory communicate with each other from a single point without interfering with each other.The e-factory has been used by Mitsubishi Electric for a long time for effective automation in its own factories, covering everything from planning to installation, operation and maintenance. Thus, while achieving high productivity, it also helps end-users to realize machine benefits and quality (www.otomasyondergisi.com, 2017). In short, the e-F@ctory concept, which is a response of Mitsubishi Electric to Industry 4.0, is an evolutionary step that can provide very significant cost savings while increasing speed, quality and productivity. This section addresses a number of examples of Industry 4.0 applications from leading companies such as Siemens, Bosch, General Electric, Festo, and Mitsubishi, and briefly mentions Industry 4.0 applications in pilot sectors in Turkey. Industry 4.0 applications are being implemented in Turkey in four pilot sectors; white appliances, machine systems, automotive and chemicals. Table 4 presents the examples of Industry 4.0 applications according to Industry 4.0 indicators such as integrated, automated and excellent production flow, virtual product design, flexible manufacturing, smart and optimized logistics and learning processes.
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from the analysis as they mostly give their own or sponsored view of Industry 4.0. However, if the national government or platform uses consultancy papers as the official source of information, they will be included in our analysis. The countries within the EU that we will focus on are Germany, Austria, Italy, Sweden, Portugal, Belgium, Luxembourg, Denmark, Hungary and Lithuania. We will look at the different kinds of initiatives similar to Industry 4.0 in these European countries, like Industria 4.0 in Portugal or MADE (Manufacturing Academy of Denmark) in Denmark. To make the document analysis more concrete and easier to analyze we created a matrix to compare the different policy documents, articles, papers and websites we found. To fill this matrix, we selected and explored the articles on the basis of their relevance for our research. We did this by first scanning all papers and the ones that either included a definition of industry 4.0, components of Industry 4.0 or links to HRM were included into our matrix (see Appendix A) All together we looked at over 100 policy papers, articles and websites etc., however relevant and included in our matrix are only 38 of those since the others did not have a definition of Industry 4.0 and or links with HRM. Moreover, we will compare which implications of Industry 4.0 each article gives for HR practices. Furthermore, we will also make use of the paper by Habraken, M., & Bondarouk T. (2017) who already analyzed smart industry and the implications on HRM in the Netherlands. I will therefore not analyze the Netherlands in my research paper, as it already has been done.
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The industrial revolutions of the past encouraged the shift from craftsmanship to mass production (Kanji, 1990), in contrast, in the last decades it seems to shift back to the old paradigm where production was more specifically tailored towards the individual (Ribeiro & Barata, 2011). This demand for more personalisation and flexibility in the production process creates challenges for existing production facilities (Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014). Nonetheless, this is the territory where the next industrial revolution, Industry 4.0, develops in. Industry 4.0 promises to provide manufacturers with rewarding business models, as well as greater efficiency, quality, customization and flexibility, but also better conditions at the workplace (Müller, Kiel, et al., 2018). However, Industry 4.0 also comes with a great deal of challenges (Kagermann et al., 2013) in the form of technological, economic, scientific, political and social challenges. For instance, the difficulties in the development of a network environment or the development of smart devices (Zhou, Liu, & Zhou, 2015). These upcoming challenges play an important role for industrial manufacturers. Especially, since they reveal reluctance and slow adaption towards this new paradigm of Industry 4.0 in manufacturing (Müller, Kiel, et al., 2018).
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In a smart factory, production capacities start interacting with manufactured goods and adapting to the new needs of consumers. At that, the whole stages of production are formed without human participation and will be deepened in this direction. This is the production part of Internet of Things, which quickly enters the life of consumer society. (Popkova, Ragulina & Bogoviz, 2018, p. 17). Without human participation means the factories have capabilities of self- awareness, self-prediction, self-comparison, self- reconfiguration and self-maintenance (Lee, Kao & Yang, 2014, pp. 4). This means, that each physical component and machine will have a twin model in the cyberspace composed of data generated from sensor networks and manual inputs. Intelligent algorithms process the data in the cyberspace, so that information about the physical components’ health condition, performance and risks are calculated and synchronized in real time. The smart machines use real-time data from their own components and other machines to gain self-awareness and self-comparison. This self-awareness enables machines to assess their own performance and to diagnose possible malfunctioning components. The machines can also predict and prevent potential failure and risk contributions to the final product. The machines can share
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Abstract As one of the most destructive technologies in the Industry 4.0 Era, robotics has been applied into higher educational practices. In this paper, we critically review the current situation of robotics application in higher education. Taking a quantitative research method, we survey both instructors and students to have a general view of how they perceive robotics in their teaching and learning process, respectively. We find no statistical differences by gender groups and country of origin for instructors and students. At last, we discuss obstacles hindering the development and wide popularization of robotics in high education institutions and put forward some tentative suggestions for the future spread of robotics in higher education. The findings of this paper would be of interest for educators, national policy makers and students.
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and distribution channels, with a large focus on the business of e-commerce. The autonomous communication between the machines supports company’s operational analysts in tracking relevant data sets to reduce errors and optimize production processes. Industry 4.0 transforms random machines into sophisticated smart machines, which share continuously information on, errors and faults, current stock levels, and changes in orders or demand levels (Deloitte, 2015, p. 4). The autonomous exchange of information between the smart machines allows for better coordination and communication of work in progress and deadlines, resulting in higher efficiency and optimising throughput times, capacity utilisation and quality in development, production, marketing and purchasing (Deloitte, 2015, p. 4). Whereas, scholars provide evidence that the initialization of Industry 4.0 shows significant improvements in product quality with limited errors and faults. As an example the Siemens electronics plant in Germany manufacturers Programmable Logic Controls (PLCs) by using digitalization processes within a smart factory. This made it possible to reduce the defects from 500 per million in 1989 to 12 defects per million in 2015, with a reliability rate of 99% (Davies, 2015). Ultimately, quality plays a crucial role within the whole process of cost reduction, as long as the defects could be reduced and savings realized it will boost also long term competiveness of German manufacturers. This aspect is also reinforced by the author Davies (2015), who reveals that the top 100 European manufactures could cut down costs by €160 billion if they are able to reduce all defects down to zero.
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As seen in this paper, and in the analyzed publications, Industry 4.0 is very likely to shape the future in various industries. Companies need to adopt Industry 4.0 technologies in order to keep their competitive advantage in an industry, and even adopt technologies in order to gain competitive advantage. However, firms shouldn’t blindly change their current processes to fit Industry 4.0, in some cases it is even unwise to do so. When a firm is a pursuing a successful cost leadership strategy it could hurt the strategy when allowing for example individualization or small batch sizes. As all firms are unique it should be thoroughly analyzed what Industry 4.0 technologies would be best for a firm to adopt, if at all. Each firm should decide which strategy it would like to pursue when adopting certain Industry 4.0 applications, and first run tests to see if the technology will strengthen a firm’s competitive position in an industry.
as they are all part of the EU, which is important and will be mentioned in more detail later. Next to this, all these countries provide online information (in English) about their national research concerning Industry 4.0. The focus was laid on policy documents from the national research agencies as it was found that definitions highly differ between countries. So, to be able to compare definitions and components of Industry 4.0 in different countries it is necessary to look (only) at the data provided by governmental institutions. Additionally, the chosen countries function as a good representation of Europe, as they are various in culture, economy and politics. Other countries that are not considered here are either collaborating with one of the above- mentioned ones, are not conducting any research in the field, or are working on a similar definition. This research depends on the availability and reliability of the online documents the countries present, as academic papers are rare, and there are no other official sources that can be used. All in all, the conducted literature review is not structured, as findings and information will evolve during the research process itself. Just at the end of the process we will be able to reflect on what was found and can then formulate a clearer definition. The decision to focus on European countries has two reasons. The first one is that, as shown before, there is simply the necessity for a comprehensive definition within Europe, as every country is using different terms and identifying different complements of Industry 4.0. This is due to all the separated, national research projects (such as Smart Industry in Sweden, Indústria 4.0 in Portugal or Made Different in Belgium). Europe is consisting out of many countries with diverse cultures, anyways, European countries are connected
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As we have noted and emphasized , modern macroeconomic theory considers all economic activities as equal, neutral. This is, obviously, based on microeconomic approach, and was accepted from macroeconomic theory, with many and far-reaching consequences. Today’s, mainstream theory forgot the old economic truth, known more than few centuries: economic activities are qualitatively different. This truth was recognized from the economic life of first European states-cities in early centuries of modern economy’s appearance and described in first economic works of Renaissance and mercantilist economists. As they emphasized, economic structure of the state is of great importance, and the industry is moving force of technological progress, an engine to economic growth and creator of synergetic effects in all economy, as it was described in 1613 by Italian mercantilist Antonio Serra . Serra was the first that emphasized the manufacturing and agriculture are subdued to different principles [16, p. 118-120]. He was the first to describe increasing returns, named after him “Law of Increasing Returns” (Senior), in contrary to diminishing returns, characterizing agriculture (Turgot). Over the next centuries, the development of today's developed part of the world was precisely the industry, which generated the innovations and technological changes, and in which the productivity of labor, in contrast to agriculture, was growing dynamically, which, in Zombart's sense, was beyond capitalism. From this development, colonies were excluded, which, despite all the stories about the metropolitan civilization role, were not allowed to build their own industry. Excluding several short periods of prevalence of the so-called laissez-faire ideology (and practical economic policies), which always ended with dire and sad consequences, the state had a very active economic role throughout this period. In addition, it is important to emphasize that the antagonism between state and market, which has characterized the twentieth and the beginning of the twenty-first century, is a relatively new phenomenon .
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With the introduction of self-aware and self-learning machines, it seems that many employees will be out of jobs soon as machines will take over tasks that were assigned to employees in the manufacturing industry. Arntz et.al. (2016) showed that the largest automation potential lies with the low educated, low income workers. However, the age of digitalisation has begun years ago and, instead of the destruction of jobs, a shift in jobs has been the case (Frey, Osborn, 2013). Frey and Osborn also state that new technologies create new jobs. Humans are significantly more flexible in their capabilities than machines. “As the most flexible entity in cyber-physical production systems, workers will be faced with a large variety of jobs ranging from specification and monitoring to verification of production strategies” (Gorecky et.al. (2014) pp1). Gorecky et.al. also state that machines will only take over specific tasks and not entire occupations. Still there is no guarantee that some jobs might be automated and those jobs tend to be low educated, low income jobs. Employees who fulfill such jobs will want to - and can- prepare themselves from the automation process. It is important to train those employees in specific tasks, that are hard to automate. Focus on tasks at the individual level and ensure that workers attain the skill requirements of tomorrow's world of work.
The ever-lasting need for organizations to adapt to its relative environment triggered a new wave of innovations, that combine the physical world to digital systems. Physical production processes like production lines are interlinked with sensors that interact with machines and humans via the internet or telecommunication networks. This stream of new technologies is embedded in the word – Industry 4.0 – which focuses on the “establishment of intelligent products and production processes (…) and flexible production (in) complex environments” (Brettel et al. 2014). Intelligent products and production processes describe automated systems that gather data during the production process. This data can be directly linked to the production manager in order to speed up the product innovation process. Flexible production describes the ability of machinery to change its purpose. Machines can change the type of product its producing or factors like capacity and volume. The term “Industry” has many several definitions and hence meanings. This Paper will focus on the definition for the term industry: “A particular form or branch or branch of economic or commercial activity” (Industry – definition by Oxford Dictionaries, 2019). Industry 4.0 is a stimulus program, triggered by the German government to boost the economy by combining physical and digital systems (Lasi et al. 2014; Brettel et al. 2014). Industry 4.0 affects all dimensions of an organization, the technical-, the organizational-, the human-, and the business model-dimension (Bischoff et al. 2015). The horizontal and vertical differentiations of the value chain are interlinked and the centralized systems become more decentralized (BMWi, 2015). Meaning that organizations or some departments in organizations are able to work closer together by Industry 4.0 applications. The control is given to local managers. Systems of this new technical revolution are the Internet of Things, Cloud computing, Artificial Intelligence, Big Data and Sensors. All with an important role in boosting efficiency in manufacturing processes and other applications by combining Industry of scale with industry of scope through the concept of “through the concept of Mass Customization”. Meaning that personalized products can be produced on massive scale, through flexible processes (Pillar, 2006; Brettel et al. 2014).
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Networks and data traffic are indispensable for Industry 4.0. Networks and data connecting machines enable data exchange. These aspects also go hand in hand with digitalization (Fernández-Miranda, Marcos, Peralta, & Aguayo, 2017). Increasingly large amounts of Information (data) can now be compressed, transported, unpacked and analyzed at high speed, at any location in the world that has access to network or data centers (Inniss & Rubenstein, 2017). Big Data is the term used to refer to this increasingly large information/data. Big Data contains a plethora of unstructured data when compared to traditional data (M. Chen, Mao, & Liu, 2014). Real-time analysis is needed for unlocking meaning and value from Big Data (Günther, Rezazade Mehrizi, Huysman, & Feldberg, 2017). As a result, Big Data creates potential challenges – particularly regarding the management and organization of the collected data. However, utilizing Big Data presents organizations with an opportunity of value creation and enables firms to gain an in-depth understanding of the Big Data in their hands (M. Chen et al., 2014).
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Considering the rapid changes in business environment and customer requirements, making right decisions, especially at very short notice, are becoming very important. Enhanced information exchange in Industry 4.0 is expected to have a huge impact on improving decision making in manufacturing operations (Jung et al. 2016). In Industry 4.0 enabled environment, technological innovations such as IoT and CPSs enable easy access to real-time information and result in effective cooperation between different machinery and manufacturing systems (Lopez Research 2014). The enhanced information sharing and integration can streamline production processes and, significantly, optimise decision making (Yan and Xue 2007). In other words, effective information exchange is considered as a strategic tool to influence the performance of production processes (Guo, Li, and Zhang 2014) as it can significantly influence production quality (Chen and Deng 2015) and product development through enabling high level of integration and improving decision making (Lang et al. 2014).
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Before answering the research question, we will look at the results found per group. In the group social and culture barriers there is a clear similarity between industry 4.0 and e-commerce as well as there is a clear similarity between industry 4.0 and environmental sustainability. This similarity also exists between e-commerce and environmental sustainability. What this shows is that the lack of (digital) culture and the lack of understanding the benefits of new innovations have a great influence on the ability for a business to adopt new development. Within the technical barrier group there only exists a similarity between industry 4.0 and e-commerce in the form of the risk of a lack of internet security and the lack of qualified employees. The missing connection between industry 4.0 and environmental sustainability is explainable because this development uses less new internet solution which create the need for extra internet security and IT personnel. Therefor, we can conclude that the barriers found in the group of e-commerce do also apply to industry 4.0. In the third group, economical barriers, there is one common barrier found among the three subject, which is the lack of resources. Furthermore, there is a similarity between industry 4.0 and e-commerce which is the uncertainty of the return on investments. In the fourth group, organisational barriers there is one similarity across the three subject, the lack of top management support and one similarity between industry 4.0 and environmental sustainability which is the lack of clear goals for the adoption of these new technologies. Overall there are many similarities found between industry 4.0 and e- commerce/environmental sustainability. Therefore, to answer the research question, it can be concluded that the perceived challenges of the implementations of industry 4.0 indeed are comparable with the challenges faced by the rise of the internet/e- commerce and environmental sustainability. Therefore, it is recommended for businesses to look further then the barriers of industry 4.0 and find out if these barriers are also found in other development. If that is the case, there is likely already a solution to overcome this barrier. The findings of this research suggest that businesses which want to adopt industry 4.0 should focus on top management involvement and acceptance of these new developments, leading the move toward industry 4.0 to convince employees of the benefits and by provide them with the right training and clear goals to achieve the fourth industrial revolution.
Martin Ford, who wrote the book “Lights in a Tunnel” in 2009, designed a scenario which talked about the implications technology like cyber-physical systems and other fast-growing technology may have on the employability of humans. In his analysis of how technology impacts peoples’ jobs, he mentions that these rapidly evolving technologies like artificial intelligence will not only affect jobs which do not require much skill (which are also usually low-wage jobs) but will also take away highly skilled jobs. This is due to the fact that the new technology will have the ability to adapt to changes and learn from mistakes which enables computers to do the same job at a faster rate compared to humans who need to go through significant training to learn how to do the job. Hence, Ford states that those jobs would be threatened by machines (Ford, 2009). Ford continues by saying that the opportunities for workers with low-skilled jobs will continue to decline due to the ongoing and increasing progress of automation within manufacturing firms. Hence, he believes that the routine jobs which are performed by low-skilled workers will be performed by technology, as these computerized machines will eventually outperform the typical employees who do those types of jobs. He concludes that there could possibly be structural unemployment due to these developments, which would not only affect people with no degrees but at all levels including those with under- and graduate degrees (Ford, 2009). Unemployment is a big problem, in 2018 there are a total of 192.3 million unemployed all around the world, this is shown in Figure 4 in Appendix A (Statista., n.d.). With an increase in automation, it is quite possible that the business models of all industries are jeopardized. This is because an increase in unemployment would lead to a decrease in product purchases as the amount of customers being able to buy products decreases. Hence, an increase in unemployment will harm the entire economy. As S.D. Simpson (2017) explains, “Unemployment leads to higher payments from state and federal governments for unemployment benefits, food assistance, and Medicaid. At the same time, those governments are no longer collecting the same levels of income tax as before - forcing the government to borrow money (which defers the costs and impacts of unemployment into the future) or cut back on other spending (perhaps exacerbating the bad economic situation).” This therefore means that countries would need to implement a new tax system with higher taxes on capital in order to be able to finance and nurture the unemployed.
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Abstract :The Fourth Industrial Revolution (I4.0) envisages fusion of technologies across the physical, digital and biological worlds, and is transforming production, management and governance into a Smart Manufacturing paradigm. It is based on exploitation of current and futuristic technologies such as Internet of Things, 3D Printing (Additive Manufacturing), 5 G connectivity, cyber security, robotics and automation. More than any other industry, defence innovation and manufacturing demand high quality and precision products. This paper brings out the need and current status of India’s defence manufacturing sector (state-controlled Defence Research and Development Organisation(DRDO), Ordnance Factories (OFs), Defence Public Sector Undertakings (DPSUs), and Private Industry including MSMEs) with regard to I4.0, and seeks to establish what needs to be done in adopting features of smart manufacturing, to make it globally competitive. Primary data obtained from a small but knowledgeable sample population, duly analysed with descriptive statistics; followed by secondary data sources, establish the influencing factors. Interpretive Structural Modelling helped formulate a framework for smart manufacturing in India’s defence industry. The paper concludes with recommendations with regard to governmental, and industry stakeholders.
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At this moment we are at the beginning of the fourth industrial revolution, also called Industry 4.0 (I4.0) or Smart Industry. I4.0 is a strategy that has to be aligned with the current strategy of the company to fully adapt to the new industrial revolution. I4.0 is defined in different ways according to literature as for example; ‘Industry 4.0 focuses on the establishment of intelligent products and production processes. In future manufacturing, factories have to cope with the need of rapid product development, flexible production as well as complex environments (Brettel, Friederichsen, Keller, & Rosenberg, 2014). Another definition; ‘Industry 4.0 describes the organisation of production processes based on technology and devices autonomously communicating with each other along the value chain: a model of the ‘smart’ factory of the future where computer-driven systems monitor physical processes, create a virtual copy of the physical world and make decentralised decisions based on self-organisation mechanisms’ (Smit, Kreutzer, Moeller, & Carlberg, 2016). But as it is summarized by the German prime minister Angela Merkel: ‘It is a way of dealing quickly with the fusion of the online world and the world of industrial production’
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To find an answer to the questions mentioned above, research was conducted which has mainly been based on the review of scientific literature. According to Webster and Watson (2002), a literature review serves as a tool to determine what prior, related research has found out, and thus, builds the base for developing new theories and frameworks. The literature used in this paper has been accessed via Scopus, Web-of-Science, Google Scholar and the research library of the University of Twente. Keywords during the search were e.g. industry 4.0; smart industry; industry 4.0 and purchasing; procurement 4.0; industry 4.0 and supply chain; etc. To further find relevant content, related articles have been browsed, and important references have been identified and taken into consideration. In addition to the usage of secondary data, at a company visit to the press brake manufacturer Wila in Lochem, questions could be asked on their approach of applying Industry 4.0. Despite their efforts to develop in a smarter direction, there was still a lack of actual application. Nonetheless, the visit gave insights into stages of development and troubles in terms of I 4.0.
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Industrial Production has come to the edge of another industrial revolution . That is fourth industrial revolution i.e. Industry 4.0, it is one of the German research initiatives to implement the high-tech strategy 2020 to meet the challenges of the 21st century. The first Industrial Revolution "Mechanization" as a result of the invention of the steam engine, the second "Mass production" with the help of electricity and the third "Digitization" by the use of Electronics and IT, these takes the dawn of the fourth Industrial Revolution through the use of cyber physical systems (CPS) and the Internet of Things and Services . The present industrial revolution (Industry 4.0) is advancement of factories or production systems by integrating them with cyber-physical systems (CPS). The basic approach of Industry 4.0 is by using the ability of cyber-physical systems to provide intelligence and communication for artificial, technical systems which are called smart systems. The processing plants (smart factories) are still being an imagination for some industries. Future production systems must be designed by considering the requirement of individualized items and, along these lines, the need for high adaptable production processes. To achieve this challenge, Cyber Physical Systems (CPSs)  should be incorporated into the industries so as to make 'Smart Factory'. A machine that gives information from the general framework and from each of its segments i.e. from the floor shop to inventory network wide (supply chain wide) incorporating the client in this framework and that allows simple access for information procurement and charge execution could be a CPPS in the connection depicted previously. CPS goes with the trend of having information and services everywhere at hand, and it is highly networked in world of today. Embedded systems, such as smart phones, cars and