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

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.
CC BY 4.0© 2024, Morteza Ghobakhloo, Masood Fathi, Mohammad Iranmanesh, Mantas Vilkas, Andrius Grybauskas and Azlan Amran.Correspondence Address: M. Ghobakhloo; Division of Industrial Engineering and Management, Uppsala University, Uppsala, Sweden; email: morteza_ghobakhloo@yahoo.comThis research has been a part of a project that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 810318.