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Managerial Challenges of Implementing Industry 4.0

Written by J. Lucke, J. Stegmüller

Paper category

Master Thesis

Subject

Business Administration>Management

Year

2020

Abstract

Thesis: The concept of "Industry 4.0" "Industry 4.0", the German version of the term was first used at the Hannover Fair in 2011 (Drath & Horch, 2014). It showcases one of the future projects formulated by the German government to play a leading role in manufacturing (Liao et al., 2017; L. D. Xu et al., 2018). However, this concept has attracted the attention of governments and industries around the world, and has been implemented in national plans under different names. Although the United States referred to their efforts in this area as the "Advanced Manufacturing Partnership", the French government soon released their initiative "New French Industry". At the same time, the UK launched the "Future of Manufacturing" movement. But Asian economic powers also focus on these new capabilities and incorporate corresponding goals into their strategies. The South Korean government announced "Manufacturing Innovation 3.0" and China through its "Made in China 2025" strategy. Similarly, Japan adopted the "Fifth Basic Science and Technology Plan" (Liao et al., 2017, pp. 3609, 3610). This brief digression aims to illustrate the importance of Industry 4.0 gaining global attention. In addition, it also shows that the terms attached to this phenomenon vary greatly depending on the investigation environment. However, the basic understanding of the concept remains basically unchanged (Liao et al., 2017) and is described in depth below. So far, there is no universally agreed definition in the literature. However, this can be explained as the unification of a large number of concepts under the term Industry 4.0 (Lasi et al., 2014). Generally, the term refers to the fourth industrial revolution, and therefore “introduction of Internet technology to industry” (Drath & Horch, 2014, p. 57). Several researchers described topics related to the changes it brought to the manufacturing process. These will again be driven by Internet technology and target real-time optimization (Kagermann, Wahlster, and Helbig, 2013; Lasi et al., 2014; L. D. Xu et al., 2018). In addition, when classifying Industry 4.0, the use of cyber-physical systems in industrial production systems is decisive for many researchers (Drath & Horch, 2014; Ghobakhloo, 2018; Kagermann et al., 2013). The definition of Jabbour et al. also emphasizes the optimization of this manufacturing process. (2018) described the key principles of industry as “horizontal and vertical integration of production systems driven by real-time data exchange and flexible manufacturing to achieve customized production” (page 19). In addition, the definition proposed in the final report of the Industry 4.0 Working Group surrounding Kagermann (2013) should be mentioned at this time. 2.3 Technology to realize Industry 4.0 There is a large amount of literature in the field of technology realization of Industry 4.0. Therefore, there are many different opinions on which type of enabling technology has the greatest impact on implementation, and therefore the most important. Wang, Wan, Li, etc. (2016) Mention the most important technologies, naming big data, artificial intelligence (AI), cloud computing and the Internet of Things (IoT). Due to the high relevance of the article (487 citations, latest revision: 07/05/2020), it provides a suitable starting point to explain the technical basis. At this point, it should be noted that due to the different names of the same phenomenon, the Internet of Things technology in this case is attributed to the use of RFID chips. Therefore, RFID technology will also be discussed in the next section. In order to make the production line efficient, even if the batch size is reduced due to personalized products, all necessary information must be considered when making production-related decisions (Brendle et al., 2016). A large part of this information, such as the production route that the product must use for further processing, what modifications must be made, and the time a pre-assembled product must wait before further processing depends on the product itself (Agostini & Filippini, 2019; Jabbour et al. , 2018; Wang, Wan, Zhang, et al., 2016). Therefore, a technology is needed to convert data from tangible objects to digital information (Gubbi, Buyya, Marusic and Palaniswami, 2013). Tangible and digital objects are combined into a framework called "Cyber ​​Physical System (CPS)". Here, the two parts are no longer distinguished from each other (Lasi et al., 2014). One technology that makes this possible is called radio frequency identification (RFID), which is used by installing the corresponding chip (Gubbi et al., 2013; Ren et al., 2015). These are attached to the manufacturing platform or the product itself and can produce and exchange data to form so-called intelligent artifacts (M. Chen, Mao, and Liu, 2014; Wang, Wan, Li, et al., 2016). With their help, a large amount of unbiased and complete data was created (Gubbi et al., 2013). This is called "big data" (Jabbour et al., 2018). These data are used to expand knowledge about the root causes of production processes and machine downtime and storage management (Wang, Wan, Li et al., 2016). Therefore, it can be said that the value of smart artefacts lies in the collection and analysis of given data (Ghobakhloo, 2018). At this time, in addition to big data and RFID, the third enabling technology-AI (Li, 2018) came into play. Although there is no uniform and universally valid definition of artificial intelligence, its origin describes the use of computers to imitate human intelligence. Read Less