An Industry 4.0 Testbed (Self-Acting Barman): Principles and Design

Kaczmarczyk, Václav; Bastan, Ondrej; Bradáč, Zdeněk; Arm, Jakub

doi:10.1016/j.ifacol.2018.07.164

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…It virtually embodies a new philosophy, transforming various branches of industry, technical standardization, safety, education, legislation, science, research, the job market, the social system and other related provinces. The onset of new technology necessitates pressure for greater flexibility in industrial production and increased cyber safety (Kaczmarczyk et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the key enablers for Industry 4.0 adoption using MICMAC analysis: a case study

Krishnan

Gupta

Mathiyazhagan³

et al. 2021

IJPPM

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PurposeThe aim of this research is to assess the key enablers of Industry 4.0 (I4.0) in the context of the Indian automobile industry. It is done to apprehend their comparative effect on executing I4.0 concepts and technology in manufacturing industries, in a developing country context. The progression to I4.0 grants the opportunity for manufacturers to harness the benefits of this industry generation.Design/methodology/approachThe literature related to I4.0 has been reviewed for the identification of key enablers of I4.0. The enablers were further verified by academic professionals. Additionally, key executive insights had been revealed by using interpretive structural modelling (ISM) model for the vital enablers unique to the Indian scenario. The authors have also applied MICMAC analysis to group the enablers of I4.0.FindingsThe analysis of this study’s data from respondents using ISM provided us with seven levels of enabler framework. This study adds to the existing literature on I4.0 enablers and findings highlight the specificities of the territories in India context. The results show that top management is the major enabler to I4.0 implementation. Infact, it occupies the 7th layer of the ISM framework. Subsequently, government policies enable substantial support to develop smart factories in India.Practical implicationsThe findings of this work provide implementers of I4.0 in the automobile industry in the form of a robust framework. This framework can be followed by the automobile sector in enhancing their competency in the competitive market and ultimately provide a positive outcome for the Indian economic development led by these businesses. Furthermore, this work will guide decision-makers in enabling strategic integration of I4.0, opening doors for the development of new business opportunities as well.Originality/valueThe study proposes a framework for Indian automobile industries. The automobile sector was chosen for this study as it covers a large percentage of the market share of the manufacturing industry in India. The existing literature does not address the broader picture of I4.0 and most papers do not provide validation of the data collected. This study thus addresses this research gap.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the key enablers for Industry 4.0 adoption using MICMAC analysis: a case study

Krishnan

Gupta

Mathiyazhagan³

et al. 2021

IJPPM

View full text Add to dashboard Cite

show abstract

“…Several research group have recently developed based on this method. Such as in multi-agent vehicle system testbed [15], [16], automation robot testbed in industry [17], [18], [19], Unmanned Aerial Vehicle (UAV) system testbeds [20], [21].…”

Section: Related Workmentioning

confidence: 99%

“…This testbed combined real and simulated information and is a real work of art toward realizing a testbed for digital, connected and adaptive production machine and production chain in industry 4.0. In the manufacturing system level [18], [19] respectively presented a digital twin of a machining process for a micro punching system and a self-acting barman robot. These manufacturing testbed, developed a cyber-physical manufacturing cloud, which combines cloud manufacturing and CPS for monitoring required signals in machine process and improve the quality and productivity of manufacturing.…”

Section: Related Workmentioning

confidence: 99%