BOTLÍKOVÁ, Milena and Josef BOTLÍK. Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems. Journal of Risk and Financial Management. Švýcarsko: MDPI, 2020, vol. 13, No 13, p. 1-39. ISSN 1911-8066. Available from: https://dx.doi.org/10.3390/jrfm13010013.
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Basic information
Original name Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems
Authors BOTLÍKOVÁ, Milena (203 Czech Republic) and Josef BOTLÍK (203 Czech Republic, guarantor, belonging to the institution).
Edition Journal of Risk and Financial Management, Švýcarsko, MDPI, 2020, 1911-8066.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 50803 Information science
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/47813059:19520/20:A0000182
Organization unit School of Business Administration in Karvina
Doi http://dx.doi.org/10.3390/jrfm13010013
UT WoS 000511892200016
Keywords (in Czech) Industry 4.0; indicator; precedenční analýza
Keywords in English Industry 4.0; indicators; precedence analysis
Tags International impact, Reviewed
Changed by Changed by: Ing. Milena Botlíková, Ph.D., učo 21108. Changed: 20/4/2021 08:50.
Abstract
In the past, the social and economic impacts of industrial revolutions have been clearly identified. The current Fourth Industrial Revolution (Industry 4.0) is haracterized by robotization, digitization, and automation. This will transform the production processes, but also the services or financial markets. Specific groups of people and activities may be replaced by new information technologies. Changes represent an extreme risk of economic instability and social change. The authors described available published sources and selected a group of indicators related to Industry 4.0. The indicators were divided into five groups and summarized by negative or positive impact. The indicators were analyzed by precedence analysis. Extremes in the geographical dislocation of actor values were found. Furthermore, spatial dependencies in the distribution of these extremes were found by calculating multiple (long) precedencies. European countries were classified according to individual groups of indicators. The results were compared with the real values of the indicators. The indicated extremes and their distribution will allow to predict changes in the behavior of the population given by changes in the socio-economic environment. The behavior of the population can be described by the behavior of autonomous systems on selected infrastructure. The paper presents research related to the creation of a multiagent model for the prediction of spatial changes in population distribution induced by Industry 4.0.
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