Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

IF 7.3 2区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Morteza Ghobakhloo, Masood Fathi, Mohammad Iranmanesh, Mantas Vilkas, Andrius Grybauskas, Azlan Amran
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Abstract

Purpose

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

Design/methodology/approach

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Findings

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

Practical implications

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

Originality/value

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

制造业中的生成人工智能:实现工业 5.0 可持续发展目标的机遇
目的本研究就生成式人工智能(AI)如何在工业 5.0 背景下加强负责任的制造提供了实用的见解。本研究采用定性-定量混合研究方法,包括案例研究、访谈和解释性结构建模(ISM),制定了一份战略路线图。该路线图可视化并阐明了生成式人工智能可促进实现工业 5.0 可持续发展目标的机制。研究结果生成式人工智能通过十种不同的功能,展示了促进实现工业 5.0 中各种可持续发展目标的能力。这些多方面的功能涉及制造业的多个方面,从提供数据驱动的生产洞察力到提高生产运营的复原力,不一而足。但是,如果按照特定顺序系统地使用这些功能,它们可以协同增效。建议制造商战略性地利用这些功能,利用它们的互补性,最大限度地发挥它们的效益。 原创性/价值 这项研究是一项创举,它提供了关于生成性人工智能如何在工业 5.0 框架内提高制造商可持续性绩效的早期实用见解。建议的战略路线图提出了优先顺序,指导制造商在决策过程中确定在何处以及出于何种目的整合生成式人工智能。
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来源期刊
Journal of Manufacturing Technology Management
Journal of Manufacturing Technology Management Engineering-Control and Systems Engineering
CiteScore
16.30
自引率
7.90%
发文量
45
期刊介绍: The Journal of Manufacturing Technology Management (JMTM) aspires to be the premier destination for impactful manufacturing-related research. JMTM provides comprehensive international coverage of topics pertaining to the management of manufacturing technology, focusing on bridging theoretical advancements with practical applications to enhance manufacturing practices. JMTM seeks articles grounded in empirical evidence, such as surveys, case studies, and action research, to ensure relevance and applicability. All submissions should include a thorough literature review to contextualize the study within the field and clearly demonstrate how the research contributes significantly and originally by comparing and contrasting its findings with existing knowledge. Articles should directly address management of manufacturing technology and offer insights with broad applicability.
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