Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing
Abstract 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 ex...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06847-z |
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| author | Muhammad Saqib Iqbal Zulhasni Abdul Rahim Qudrattullah Omerkhel Hamza Iftikhar Muhammad Fawad Khan |
| author_facet | Muhammad Saqib Iqbal Zulhasni Abdul Rahim Qudrattullah Omerkhel Hamza Iftikhar Muhammad Fawad Khan |
| author_sort | Muhammad Saqib Iqbal |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-aea2e2b9b76f4ae4afead25eb2909021 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-aea2e2b9b76f4ae4afead25eb29090212025-08-20T03:10:35ZengSpringerDiscover Applied Sciences3004-92612025-06-017611710.1007/s42452-025-06847-zLeveraging AI and TRIZ for sustainable innovation in advanced manufacturingMuhammad Saqib Iqbal0Zulhasni Abdul Rahim1Qudrattullah Omerkhel2Hamza Iftikhar3Muhammad Fawad Khan4NUST Business School, National University of Sciences & TechnologyMalaysia-Japan International Institute of Technology, Universiti Teknologi MalaysiaFaculty of Computer Sciences, Kabul Education UniversityS3H, National University of Sciences & TechnologyNUST Business School, National University of Sciences & TechnologyAbstract 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.https://doi.org/10.1007/s42452-025-06847-zAI-driven manufacturingPatent analyticsTRIZ methodologySustainable industry 4.0 |
| spellingShingle | Muhammad Saqib Iqbal Zulhasni Abdul Rahim Qudrattullah Omerkhel Hamza Iftikhar Muhammad Fawad Khan Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing Discover Applied Sciences AI-driven manufacturing Patent analytics TRIZ methodology Sustainable industry 4.0 |
| title | Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing |
| title_full | Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing |
| title_fullStr | Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing |
| title_full_unstemmed | Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing |
| title_short | Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing |
| title_sort | leveraging ai and triz for sustainable innovation in advanced manufacturing |
| topic | AI-driven manufacturing Patent analytics TRIZ methodology Sustainable industry 4.0 |
| url | https://doi.org/10.1007/s42452-025-06847-z |
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