Modeling the Social Consequences of Industrial Robotization

Тихонова, Анна

doi:10.2991/aebmr.k.201205.038

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The major markets for industrial robots are China, Japan, the US, the Republic of Korea, and Germany. These economies account for approximately 74% of robot installations worldwide (Tikhonova, 2020). It is, therefore, necessary to give special attention to technologies and the implications of their use.…”

Section: Robotizationmentioning

confidence: 99%

Impact of Robot Installations on Employment and Labour Productivity in Automotive Industry

Pisková,

Dobranschi,

Semerád

et al. 2024

CEBR

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The automotive industry is regarded as one of the most robotized sectors of the manufacturing industry. The topic of robotization is currently intensely debated within the Fourth Industrial Revolution or Industry 4.0. In this study, we focused on automobile manufacturers operating in selected countries of the European Union and analysed the level of labour productivity and employment in the period 2002-2021. Specifically, we investigated whether the annual installation of robots and the total number of robots in the automotive industry had a negative impact on labour demand. The collected data were evaluated on the basis of empirical analysis. Our estimations show that the installation of robots in the automotive industry has a positive effect on labour demand. At the same time, however, it was found that labour productivity tends to decrease as the number of robots increases. A possible explanation for these results is that major manufacturing activities in this sector are being replaced by machines at the expense of human labour. Implications for Central European audience: Theoretical implications of the article arise from primary literature that investigates the implications of industry robotization on the labour market. The practical implications stem from our empirical analysis, which investigates the impact of robotization on labour demand and productivity in automobile industries across the EU.

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Section: Robotizationmentioning

confidence: 99%

Impact of Robot Installations on Employment and Labour Productivity in Automotive Industry

Pisková,

Dobranschi,

Semerád

et al. 2024

CEBR

View full text Add to dashboard Cite

show abstract

“…Since it permits time-dynamic behavior, multi-axis robot arms have been successfully used for trajectory planning and obstacle avoidance. It can be stated that the main reasons for using industrial robot units re economic, as detailed in the annual report of the International Federation of Robotics (IFR) [2] The above do not follow as a rule from the integration of an industrial robot into a production line, but they represent the current aspirations of industrial production and together determine the direction of its development.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

et al. 2022

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In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

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Modeling the Social Consequences of Industrial Robotization

Cited by 2 publications

References 7 publications

Impact of Robot Installations on Employment and Labour Productivity in Automotive Industry

Impact of Robot Installations on Employment and Labour Productivity in Automotive Industry

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

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