Competitiveness of Domestic Enterprises in Changing Markets and Industry 4.0

Ćoćkalo, Dragan; Đorđević, Dejan; Bogetić, Srđan; Bakator, Mihalj; Bešić, Cariša

doi:10.1007/978-3-030-18180-2_9

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…As a consequence of the globalization, technologies are implemented faster in the manufacturing industry in order to be competitive and to respond to market demands [ 16 ]. In this sense, the technologies used in the scientific literature aim to enhance manufacturing processes with different strategies (i.e., Lean manufacturing, Six-sigma) [ 17 ] without considering the coordination between machinery and services.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin for Automatic Transportation in Industry 4.0

Martínez-Gutiérrez

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

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Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.

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Section: Introductionmentioning

confidence: 99%

Digital Twin for Automatic Transportation in Industry 4.0

Martínez-Gutiérrez

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In the meantime, with the development and the diversification of global market in recent decades [10], the requirements for product are becoming more smart, complex and customized. In this context, the company must consolidate and strengthen its competitiveness by delivering the products that fulfil the requirements with high operational efficiency and delivery quality [11].…”

Section: Introductionmentioning

confidence: 99%