Artificial Intelligence-driven Big Data Analytics, Real-Time Sensor Networks, and Product Decision-Making Information Systems in Sustainable Manufacturing Internet of Things

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F21%3A00002170" target="_blank" >RIV/75081431:_____/21:00002170 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.addletonacademicpublishers.com/contents-emfm/2216-volume-16-3-2021/4071-artificial-intelligence-driven-big-data-analytics-real-time-sensor-networks-and-product-decision-making-information-systems-in-sustainable-manufacturing-internet-of-thing" target="_blank" >https://www.addletonacademicpublishers.com/contents-emfm/2216-volume-16-3-2021/4071-artificial-intelligence-driven-big-data-analytics-real-time-sensor-networks-and-product-decision-making-information-systems-in-sustainable-manufacturing-internet-of-thing</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Intelligence-driven Big Data Analytics, Real-Time Sensor Networks, and Product Decision-Making Information Systems in Sustainable Manufacturing Internet of Things

Popis výsledku v původním jazyce

We develop a conceptual framework based on a systematic and comprehensive literature review on artificial intelligence-driven big data analytics, real-time sensor networks, and product decision-making information systems in sustainable manufacturing Internet of Things. Building our argument by drawing on data collected from Management Events and McKinsey, we performed analyses and made estimates regarding how reliable and resilient smart factories develop on deep learning-based autonomous assembly systems. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Název v anglickém jazyce

Artificial Intelligence-driven Big Data Analytics, Real-Time Sensor Networks, and Product Decision-Making Information Systems in Sustainable Manufacturing Internet of Things

Popis výsledku anglicky

We develop a conceptual framework based on a systematic and comprehensive literature review on artificial intelligence-driven big data analytics, real-time sensor networks, and product decision-making information systems in sustainable manufacturing Internet of Things. Building our argument by drawing on data collected from Management Events and McKinsey, we performed analyses and made estimates regarding how reliable and resilient smart factories develop on deep learning-based autonomous assembly systems. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Economics, Management, and Financial Markets

ISSN

1842-3191

e-ISSN

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Svazek periodika

16

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

81-93

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85115866485