Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing
Artificial intelligence (AI) is an emerging technology that has been widely used in the field of manufacturing. In this paper, we explore the integration of AI in sustainable manufacturing using TRIZ S-curve analysis and insights from a Delphi survey of industry experts.In this paper, we explore the integration of AI in sustainable manufacturing using TRIZ S-curve analysis and insights from a Delphi survey of industry experts. The results underscore the need for a multidisciplinary approach to match artificial intelligence innovations with sustainable development goals, guaranteeing not only efficiency but also inclusivity and long-term society impact with the more general objectives of sustainable development and responsible innovation.The results underline the need for a multidisciplinary approach to match artificial intelligence innovations with sustainable development goals, guaranteeing not only efficiency but also inclusivity and long-term society impact with the more general objectives of sustainable development and responsible innovation. The key themes emerged: democratisation of AI, ethical AI deployment, and alignment with the Sustainable Development Goals (SDGs). The paper proposes actionable strategies to overcome barriers to adopting artificial intelligence in manufacturing, such as the need for internal data science expertise and ethical considerations, ultimately contributing to sustainable industry practices.
Authors
- Zulhasni Abdul Rahim (ORCID: https://orcid.org/0000-0002-4391-8589)
- Muhammad Saqib Iqbal (ORCID: https://orcid.org/0000-0001-7958-4114)
- Hamza Iftikhar (ORCID: https://orcid.org/0000-0001-8278-3747)
- Muhammad Fawad Khan (ORCID: https://orcid.org/0000-0002-2482-5977)
- Qudrattullah Omerkhel
Institutions
- Kabul University (AF)
- Kabul Education University (AF)
- National University of Technology (PK)
- University of Kuala Lumpur (MY)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- Discover Applied Sciences
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1007/s42452-025-06847-z
- Citations
- 3
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 3.73