J 2020

Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems

BOTLÍKOVÁ, Milena a Josef BOTLÍK

Základní 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, domácí) a Josef BOTLÍK (203 Česká republika, garant)

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/47813059:19240/20:A0000642

Organizační jednotka

Filozoficko-přírodovědecká fakulta v Opavě

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: 19. 4. 2021 22:32, 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.