Industry 4.0 and its Implementation: a Review
TL;DR: In this article, the authors present a systematic review of the scope of Industry 4.0, its goals and implementations, as well as the barriers to the implementation of Industry 5.0.
Abstract: Triggered by the development of information and communications technologies, Industry 4.0 opens up a new era for the manufacturing industry.Currently, Industry 4.0 has attracted much attention from industry and academia. Research on Industry 4.0 is still evolving towards the development of frameworks linking Industry 4.0’s enabling technologies to specific goals and to their impact on the manufacturers’ businesses.Accordingly, this study presents a systematic review of the scope of Industry 4.0, its goals and implementations, as well as the barriers to the implementation of Industry 4.0. Solutions for overcoming the barriers and challenges are discussed.
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TL;DR: In this paper , the authors characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business, including supply chains, healthcare, secure transactions, and finance and accounting.
Abstract: Abstract Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject’s intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.
36 citations
TL;DR: In this article , the authors conducted a multiple case analysis with interviews and document data from four different types of manufacturing companies with open innovation enabled by digitalization capabilities, providing two key contributions to extant literature.
32 citations
31 citations
TL;DR: In this article, the authors examined the existing challenges of AI implementation in PMS of India and explored the inter-relationships among them, and proposed a model for industrial decision-makers and managers to take appropriate decisions to develop intelligent AI enabled systems for manufacturing organizations in emerging economies.
21 citations
TL;DR: In this article, three models: long short-term memory (LSTM), convolutional neural network (CNN), and combined CNN-lSTM are proposed for classification of human activities.
Abstract: According to the Industry 4.0 vision, humans in a smart factory, should be equipped with formidable and seamless communication capabilities and integrated into a cyber-physical system (CPS) that can be utilized to monitor and recognize human activity via artificial intelligence (e.g., deep learning). Recent advances in the accuracy of deep learning have contributed significantly to solving the human activity recognition issues, but it remains necessary to develop high performance deep learning models that provide greater accuracy. In this paper, three models: long short-term memory (LSTM), convolutional neural network (CNN), and combined CNN-LSTM are proposed for classification of human activities. These models are applied to a dataset collected from 36 persons engaged in 6 classes of activities – downstairs, jogging, sitting, standing, upstairs, and walking. The proposed models are trained using TensorFlow framework with a hyper-parameter tuning method to achieve high accuracy. Experimentally, confusion matrices and receiver operating characteristic (ROC) curves are used to assess the performance of the proposed models. The results illustrate that the hybrid model CNN-LSTM provides a better performance than either LSTM or CNN in the classification of human activities. The CNN-LSTM model provides the best performance, with a testing accuracy of 97.76%, followed by the LSTM with a testing accuracy of 96.61%, while the CNN shows the least testing accuracy of 94.51%. The testing loss rates for the LSTM, CNN, and CNN-LSTM are 0.236, 0.232, and 0.167, respectively, while the precision, recall, $F1$ -Measure, and the area under the ROC curves (AUCS) for the CNN-LSTM are 97.75%, 97.77%, 97.76%, and 100%, respectively.
20 citations
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TL;DR: A comprehensive review on Industry 4.0 is conducted and presents an overview of the content, scope, and findings by examining the existing literatures in all of the databases within the Web of Science.
1,906 citations
TL;DR: The state of the art in the area of Industry 4.0 as it relates to industries is surveyed, with a focus on China's Made-in-China 2025 and formal methods and systems methods crucial for realising Industry 5.0.
Abstract: Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...
1,780 citations
11 Nov 2014-World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering
TL;DR: In this paper, the authors describe the developments of Industry 4.0 within the literature and review the associated research streams. And they assess the practical implications, conducting face-to-face interviews with managers from the industry as well as from the consulting business.
Abstract: The German manufacturing industry has to withstand an increasing global competition on product quality and production costs. As labor costs are high, several industries have suffered severely under the relocation of production facilities towards aspiring countries, which have managed to close the productivity and quality gap substantially. Established manufacturing companies have recognized that customers are not willing to pay large price premiums for incremental quality improvements. As a consequence, many companies from the German manufacturing industry adjust their production focusing on customized products and fast time to market. Leveraging the advantages of novel production strategies such as Agile Manufacturing and Mass Customization, manufacturing companies transform into integrated networks, in which companies unite their core competencies. Hereby, virtualization of the processand supply-chain ensures smooth inter-company operations providing real-time access to relevant product and production information for all participating entities. Boundaries of companies deteriorate, as autonomous systems exchange data, gained by embedded systems throughout the entire value chain. By including Cyber-PhysicalSystems, advanced communication between machines is tantamount to their dialogue with humans. The increasing utilization of information and communication technology allows digital engineering of products and production processes alike. Modular simulation and modeling techniques allow decentralized units to flexibly alter products and thereby enable rapid product innovation. The present article describes the developments of Industry 4.0 within the literature and reviews the associated research streams. Hereby, we analyze eight scientific journals with regards to the following research fields: Individualized production, end-to-end engineering in a virtual process chain and production networks. We employ cluster analysis to assign sub-topics into the respective research field. To assess the practical implications, we conducted face-to-face interviews with managers from the industry as well as from the consulting business using a structured interview guideline. The results reveal reasons for the adaption and refusal of Industry 4.0 practices from a managerial point of view. Our findings contribute to the upcoming research stream of Industry 4.0 and support decisionmakers to assess their need for transformation towards Industry 4.0 practices. Keywords—Industry 4.0., Mass Customization, Production networks, Virtual Process-Chain. Malte Brettel, chairholder, is with the Aachen University (RWTH), Kackertstraße 7, 52072 Aachen (e-mail: brettel@win.rwth-aachen.de). Niklas Friederichsen is with the Aachen University (RWTH), Kackertstraße 7, 52072 Aachen, (corresponding author; phone: +49/(0)241 80 99397; e-mail: friederichsen@win.rwth-aachen.de). Michael Keller and Marius Rosenberg are with the Aachen University (RWTH), Kackertstraße 7, 52072 Aachen (e-mail: keller@win.rwthaachen.de, rosenberg@win.rwth-aachen.de).
1,184 citations
TL;DR: A smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop-floor objects such as machines, conveyers, and products is presented and an intelligent negotiation mechanism for agents to cooperate with each other is proposed.
1,074 citations