AI-Driven Manufacturing: Surveying for Industry 4.0 and Beyond

Arti Bandhana () and Jiří Vokřínek ()
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Arti Bandhana: Czech Technical University
Jiří Vokřínek: Czech Technical University

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-31

Abstract: Abstract 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.

Keywords: Industry 4.0; MAS–MES integration; Multiagent systems; Manufacturing execution systems; Smart manufacturing; Human–machine collaboration; Industrial revolutions (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00554-6

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