AI-Driven Manufacturing: Surveying for Industry 4.0 and Beyond
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00385731" target="_blank" >RIV/68407700:21230/25:00385731 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s43069-025-00554-6" target="_blank" >https://doi.org/10.1007/s43069-025-00554-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s43069-025-00554-6" target="_blank" >10.1007/s43069-025-00554-6</a>
Alternative languages
Result language
angličtina
Original language name
AI-Driven Manufacturing: Surveying for Industry 4.0 and Beyond
Original language description
Artificial intelligence is transforming various industries, including manufacturing, yet its full potential in manufacturing remains underutilized. Industry 4.0 aims to enhance productivity, operational efficiency, and decision-making, but achieving these objectives at scale remains an ongoing challenge. This paper surveys the artificial intelligence-driven integration of multi-agent systems and manufacturing execution systems as key enablers of smart manufacturing in Industry 4.0 and the emerging Industry 5.0. It reviews state-of-the-art developments, identifies key challenges, and outlines research priorities by analyzing trends from past industrial revolutions. The paper also emphasizes workforce upskilling and advocates for a problem-driven approach, prioritizing solving practical challenges over pursuing technological innovation without clear objectives. Furthermore, this paper leverages responses generated via ChatGPT and Microsoft Copilot to assess whether the discussions presented align with AI-generated insights, demonstrating an example of human–machine collaboration to set a precedent for future research. Concluding with a forward-looking agenda, it emphasizes the need for high-technology readiness level pilot implementations and stronger industry-academia collaboration to transition from theoretical breakthroughs to large-scale industrial deployment. This paper serves as a guide for researchers, policymakers, and stakeholders, providing a comprehensive perspective on technological advancements, integration challenges, and industry standards in smart manufacturing.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Operations Research Forum
ISSN
2662-2556
e-ISSN
2662-2556
Volume of the periodical
6
Issue of the periodical within the volume
4
Country of publishing house
CH - SWITZERLAND
Number of pages
31
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105017153492