2020
Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems
BOTLÍKOVÁ, Milena a Josef BOTLÍKZákladní údaje
Originální název
Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems
Autoři
BOTLÍKOVÁ, Milena (203 Česká republika) a Josef BOTLÍK (203 Česká republika, garant, domácí)
Vydání
Journal of Risk and Financial Management, Švýcarsko, MDPI, 2020, 1911-8066
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
50803 Information science
Stát vydavatele
Švýcarsko
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Kód RIV
RIV/47813059:19520/20:A0000182
Organizační jednotka
Obchodně podnikatelská fakulta v Karviné
UT WoS
000511892200016
Klíčová slova česky
Industry 4.0; indicator; precedenční analýza
Klíčová slova anglicky
Industry 4.0; indicators; precedence analysis
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 20. 4. 2021 08:50, Ing. Milena Botlíková, Ph.D.
Anotace
V originále
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.