The evolution of production systems from Industry 2.0 through Industry 4.0

Yin, Yong; Stecke, Kathryn E.; Li, Dongni

doi:10.1080/00207543.2017.1403664

Cited by 528 publications

(263 citation statements)

References 47 publications

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“…Driven by the differing market requirements over the last years, manufacturing systems have faced broad changes (Yin et al 2018), from the introduction of assembly lines to the costeffectiveness requirements of mass production, the introduction and discussions of balanced and unbalanced lines (Davis 1965), and the establishment of JIT lines based on the "Toyota Production System" (Ono 1988), which aligns production with demand to eliminate waste. Thereby, different production line configurations, such as serial, parallel with or without crossover have been introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Through Industry 4.0 connectivity, automation, fast information exchange and analytics, a new dimension of flexibility can be reached and new approaches to planning and controlling production systems designed. Cloud-based manufacturing is a technology which can contribute significantly to the realization of Industry 4.0 advantages (Thames and Schaefer 2016;Yin et al 2018;Shukla et al 2019;Ivanov and Dolgui 2020;Ivanov et al 2016Ivanov et al , 2018b. The aspiration of cloud manufacturing is to form production networks capable of dynamic reconfiguration and high flexibility, while intelligent big data analytics can provide global feedback to achieve high efficiency (Wang et al 2016;Ahn et al 2018;De Sousa Jabbour et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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“…In particular, the promising application of Internet of Things paradigms to industry can be useful in this sense (Atzori, Iera, and Morabito 2010;Bi, Da Xu, and Wang 2014;Ardanza et al 2019). Such workplaces should make greater use of augmented reality and VR, coupled with ergonomic simulation (as emphasised in Section 3) and supported by new age-oriented ergonomics assessment methods (Garbie 2014;Kong 2018;Yin, Stecke, and Li 2018). In this future industrial setting, the digitalisation of work environments will revolutionise the innovation of manufacturing and assembly work cells and the development and integration of all the equipment inside the workspace (EC 2015a).…”

Section: Research Area 4: New Age-friendly User-centred Workpace Dementioning

confidence: 99%

Ageing workforce management in manufacturing systems: state of the art and future research agenda

Calzavara

Battini

Bogataj

et al. 2019

International Journal of Production Research

140

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The workforce ageing phenomenon is recently affecting most of the Organisation for Economic Co-operation and Development (OECD) member countries, due to a general ageing of their populations and a higher average retirement age of the workforce. In this paper, the topic of ageing workforce management is addressed from a production research standpoint, with the aim of understanding how older workers can be supported and involved in a manufacturing system. First, the current state of the art related to the ageing workforce in production systems is presented. This is structured according to four main topics: (1) analysis and evaluation of ageing workers' functional capacities, (2) consideration of ageing workers' capacities in industrial system modelling and management, (3) analysis and exploitation of ageing workers' expertise, (4) acknowledgement, analysis, design and integration of supporting technologies. Next, the discussion on the impact of the ageing workforce on manufacturing systems' performances leads to the comparison of some technological advances that are related to the Industry 4.0 paradigms. Finally, a future research agenda on this topic is proposed, based on the same topics classification proposed for the literature analysis. Five different research areas are derived, suggesting future directions for appropriate research concerning the employ of older workers in production environments.

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“…Sinsel et al (2017) and Magone (2016) determined that the central perspective debated about Industry 4.0 is a technological perspective, although some studies on the management and economic evaluation of the smart factories exist. Most of them deal with the topic of supply chain management such as interconnections of cross-company supply chains (Bauer, Herkommer, & Schlund, 2015), opportunities for circular supply chains (Tsolakis, Kumar, & Srai, 2016), supply chain dynamics (Ivanov et al, 2016), sustainable supply chains (Bechtsis, Tsolakis, Vlachos, & Iakovou, 2017), followed by product individualization (e.g., Emmelmann, Rudolph, & Herzog, 2017), and business process optimization (e.g., Yin, Stecke, & Li, 2018). However, the aim of smart factories is also to consider the human aspect of the new paradigm brought by the fourth industrial revolution (Kang, Lee, Choi, Kim, & Noh, 2016), which is currently underresearched.…”

Section: Concept Of Smart Factory Systemmentioning

confidence: 99%

Transformation towards smart factory system: Examining new job profiles and competencies

2019

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This paper increases understanding of the concept of smart factories system and its implementation in the automotive industry in terms of addressing necessary changes in human aspect of system, namely, employees' competencies, as well as to new job profiles. A qualitative research approach was used by utilizing a case study of the automotive industry in Slovenia. For the purpose of data collection, we used a semistructured interview on a sample of experts from governmental, educational, and private automotive sectors. In order to analyse gathered data, we used the method of content analysis. Research results indicate that significant changes are essential in the transformation from traditional to a smart factory system, especially regarding organizational mindset, the architecture of system and its processes, and the inclusion of Industry 4.0 technologies at all levels. Also, results indicate changes are essential regarding future job profiles and competencies. Although there are different perceptions about future jobs and competencies needed for Industry 4.0‐induced changes, depending on the sector in which the expert is employed, there is an overall agreement that job profiles related to programming, mechatronics, robotics, data analysis, Internet of Things, design, and maintenance of smart systems, process analysis, and bionics are the new job profiles needed in smart factory systems. In addition to the high emphasis on technical skill and knowledge, soft competencies are considered important, as well as competencies of continues learning, flexibility, creativity, problem solving, critical, and analytical thinking. Our findings possess important implications for conceptualizing job profiles and competencies in smart factories systems and practical implications for managers concerned with human resource management activities. We provide an outlook to the future job profiles that are underresearched, especially in the field of smart manufacturing. As well, our paper provides insight into the importance of different competencies recognized as crucial for complete functioning and optimal performance of smart factories system. This study provides a starting point for further research regarding human resource management issues in smart factories in the automotive sector, especially related to future job profiles and employee competencies, and their role in system performance.

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The evolution of production systems from Industry 2.0 through Industry 4.0

Cited by 528 publications

References 47 publications

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Ageing workforce management in manufacturing systems: state of the art and future research agenda

Transformation towards smart factory system: Examining new job profiles and competencies

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