Implementing Industry 4.0 in Discrete Manufacturing: Options and Drawbacks

Arm, Jakub; Zezulka, F.; Bradáč, Zdeněk; Marcoň, P.; Kaczmarczyk, Václav; Benešl, Tomáš; Schroeder, Thomas

doi:10.1016/j.ifacol.2018.07.106

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…Other authors used terms like 'high-sky-vision' or 'visionary concept' to underline the theoretical background and the lack of reference to the real production environment [56,57]. A frequently used term is 'concept' or 'umbrella' of Industry 4.0 [56][57][58][59][60][61]. Descriptions vary in their focus, their scope and their information density, which slows down the academic progress [47].…”

Section: Structuring Industry 40mentioning

confidence: 99%

Industry 4.0: A Technological-Oriented Definition Based on Bibliometric Analysis and Literature Review

Rupp

Schneckenburger

Merkel

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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Industry 4.0 was introduced in 2011 and since then has been perceived in multiple ways such as a vision, a paradigm, a scenario or as a digital revolution in production and service organizations. Even though Industry 4.0 is associated with great improvements for companies, there is still a lack of a uniform definition of the term Industry 4.0, especially when it comes to the transfer of knowledge from theoretical research to the implementation in organizations, which leads to confusion and disaffirmation. The lack of a clear structure and a holistic definition of the research topic Industry 4.0 inhibits the development of new business areas and new research approaches. To target this fundamental gap, a methodology is developed and the 338 most relevant publications are analyzed in the database of ScienceDirect starting from 2015. Based on those publications, the field of Industry 4.0 is structured. A consistent and comprehensive definition for Industry 4.0 is introduced by using a bibliometric analysis. Therefore, existing descriptions are decomposed into word fragments and analyzed. It is shown that this novel approach to find a definition for the term “Industry 4.0” does not yet exist. The aim is to provide a purely objective definition based on a statistical evaluation, without restricting the selection of publications to a specific research or business area. Based on those data, a new and ubiquitous definition of Industry 4.0 is formed, discussed and validated on practical examples.

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Section: Structuring Industry 40mentioning

confidence: 99%

Industry 4.0: A Technological-Oriented Definition Based on Bibliometric Analysis and Literature Review

Rupp

Schneckenburger

Merkel

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

“…The amount of generated data from manufacturing systems increases simultaneously based on the increasing data amount in IoT technology and sensors. This data procedure system is famous for big data [32][33][34][35][36]. Processing the generated data is one of the difficulties that need to be addressed.…”

Section: Big Data In Manufacturingmentioning

confidence: 99%

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

Shahbazi

Byun

2021

Applied Sciences

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The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

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“…Real-time traffic filtering through field firewall tools is used in the mitigation process, and data from decentralized dedicated servers is correlated for accurate decision-making [13]. In order to break the deadlock in Industry 4.0, a job shop scheduling method is adopted, and a new assessment feature is created, using the Asset Administrative Shell (AAS) [14]. Logistics 4.0 is the result of the IIoT revolutionizing the logistic domains by offering accountability (supply chain visibility), integrity management, and commodity delivery cost.…”

Section: Related Workmentioning

confidence: 99%

Determining the Degree of Characteristics for Internet of Healthcare Devices Using Fuzzy ANP

Lin

2021

Scientific Programming

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With the revolution in Internet and digital technology, every organization is adopting digital things to carry out their day-to-day activities. Internet of Things (IoT) is a concept that is used to connect various devices over the Internet to increase the production and quality of service, deliver a huge amount of data in seconds, and automate the processes. IoT implementation in the health sector has changed the typical setup into smart and intelligent setup. With the “smart” and “Intelligent” abilities of IoT devices such as sensors and with the collaboration of humans and computers, physical processes can be monitored and, based on the received data, optimal decisions can ultimately be taken. IoT applications in healthcare will increase flexibility, patient’s care, quality of health, and control of diseases. As the IoT combines various heterogeneous devices and the inappropriate determination of the degree of characteristics for Internet of healthcare (IoH) devices may affect the efficiency of services, this research is carried out with the use of decision support system and application of fuzzy analytic network process (fuzzy ANP) was used for optimal determination of the degree of characteristics for IoH devices based on unique properties which would greatly increase the efficiency of industry. The experimental results are efficient and show the usefulness of the approach.

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Implementing Industry 4.0 in Discrete Manufacturing: Options and Drawbacks

Cited by 13 publications

References 3 publications

Industry 4.0: A Technological-Oriented Definition Based on Bibliometric Analysis and Literature Review

Industry 4.0: A Technological-Oriented Definition Based on Bibliometric Analysis and Literature Review

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

Determining the Degree of Characteristics for Internet of Healthcare Devices Using Fuzzy ANP

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