Human-centred design in industry 4.0: case study review and opportunities for future research

Nguyen, Hien Ngoc; Erle, Ganix Lasa; Iriarte, Ion

doi:10.1007/s10845-021-01796-x

Cited by 77 publications

(39 citation statements)

References 167 publications

(345 reference statements)

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“…With the aid of a questionnaire, we identify the current challenges faced by companies in production systems. Companies are keen to implement innovative innovations to boost resource quality, productivity, and efficiency, reduce risk, and stay competitive [ 16 , 108 , 109 , 110 , 111 ]. A business that struggles to deal with technology complexities also faces implementing new products/services, creativity, and business models, bringing the organization into a fierce competition where expenses have to be reduced each year [ 112 , 113 , 114 , 115 ].…”

Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

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Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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show abstract

“…While different segments or domains of the manufacturing industry may have different needs, they are all expected to leverage connectedness and access to real-time insights across systems, processes, partners, products, and people. The goal is to enable mass-customization of products and services; adopt automatic and flexible production lines (Escobar et al, 2021); track parts and products in realtime; facilitate communication among parts, products, and machines; enhance human-machine collaboration (Nguyen Ngoc et al, 2021); achieve IoT-enabled production optimization in smart factories (Yao et al, 2019); create new types of services and business models of interaction in value chains (Alcácer & Cruz-Machado, 2019); achieve a higher level of intelligent automation; and ultimately increase quality, productivity, flexibility, lower costs, and higher efficiency (Barari et al, 2021).…”

Section: Motivation: Technology Trends In Manufacturingmentioning

confidence: 99%

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Yuan

Kamarthi

et al. 2022

J Intell Manuf

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In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing.

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“…These requirements begin with training, then assign the tasks, and finally monitor the human-machine interconnection to optimize the overall production or manufacturing processes. However, and according to [27], [28] and [29] there is still a lack of research that outline the overall human requirements, especially with those related to ethics .…”

Section: A Industry 40 Requirementsmentioning

confidence: 99%

Industrial Internet of Things: Requirements, Architecture, Challenges, and Future Research Directions

2022

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Industry 4.0 relates to the digital revolution of manufacturing and other markets such as retail, distribution, oil and gas, and infrastructure. On the other hand, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector's productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IoT characteristics. We offer an in-depth review of Industry 4.0 and IIoT in this survey. The primary motivation behind this survey is to introduce the most recent advancements related to Industry 4.0 and IIoT and to address the existing limitations. First, we provide a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Second, the study provides a comprehensive review to study the state-of-the-art of Industry 4 in terms of concepts, requirements and supporting technology. In addition, we discuss the correlation between enabling technology and technical requirements in detail. Finally, our survey targets deep learning, edge computing, and big data as key techniques for future IIoT. Furthermore, the presented technique is thoroughly examined to present a different method for future adoption. Additionally, to the proposed technique, we propose a new architecture for the future of IIoT based on three primary techniques.

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Human-centred design in industry 4.0: case study review and opportunities for future research

Cited by 77 publications

References 167 publications

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Industrial Internet of Things: Requirements, Architecture, Challenges, and Future Research Directions

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