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2025 | OriginalPaper | Buchkapitel

Integrating Generative AI with TRIZ for Evolutionary Product Design

verfasst von : Marin Iuga, Stelian Brad

Erschienen in: World Conference of AI-Powered Innovation and Inventive Design

Verlag: Springer Nature Switzerland

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Abstract

Democratizing, scaling, and automating creativity and innovation are essential for boosting competitiveness, efficiency, and cost-effectiveness in product creation. This capability will be a key differentiator for organizations seeking a competitive edge and a catalyst for ensuring a better future for humanity. We demonstrate how the TRIZ methodology and Generative AI provide a solid foundation for solving product design challenges. We show how technology and human feedback enable the automated discovery of product design improvements. To prove this, we have developed a technological proof-of-concept solution that leverages the strengths of both TRIZ and Generative AI. This solution explores product reviews, identifies product strengths and weaknesses, and generates an initial palette of potential product design problems that can be iteratively refined to drive product improvement decisions. We provide the theoretical foundation by utilizing TRIZ’s systematic problem-solving approach and specific examples of generated data, creating a consistent basis for evaluating the tremendous potential of Generative AI and TRIZ in product design and improvement processes.

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Literatur
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Zurück zum Zitat Dewulf, S., Childs, P.R.: Innovation logic: benefits of a TRIZ-like mind in AI using text analysis of patent literature. In: Cavallucci, D., Livotov, P., Brad, S. (eds.) TFC 2023. IFIP Advances in Information and Communication Technology, vol. 682, pp. 95–102. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42532-5_7 Dewulf, S., Childs, P.R.: Innovation logic: benefits of a TRIZ-like mind in AI using text analysis of patent literature. In: Cavallucci, D., Livotov, P., Brad, S. (eds.) TFC 2023. IFIP Advances in Information and Communication Technology, vol. 682, pp. 95–102. Springer, Cham (2023). https://​doi.​org/​10.​1007/​978-3-031-42532-5_​7
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Zurück zum Zitat Ghane, M., Ang, M.C., Cavallucci, D., Kadir, R.A., Ng, K.W., Sorooshian, S.: TRIZ trend of engineering system evolution: a review on applications, benefits, challenges and enhancement with computer-aided aspects. Comput. Ind. Eng. 174, 108833 (2022)CrossRef Ghane, M., Ang, M.C., Cavallucci, D., Kadir, R.A., Ng, K.W., Sorooshian, S.: TRIZ trend of engineering system evolution: a review on applications, benefits, challenges and enhancement with computer-aided aspects. Comput. Ind. Eng. 174, 108833 (2022)CrossRef
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Zurück zum Zitat Ghane, M., Ang, M.C., Cavallucci, D., Kadir, R.A., Ng, K.W., Sorooshian, S.: Semantic TRIZ feasibility in technology development, innovation, and production a systematic review. Heliyon (2023) Ghane, M., Ang, M.C., Cavallucci, D., Kadir, R.A., Ng, K.W., Sorooshian, S.: Semantic TRIZ feasibility in technology development, innovation, and production a systematic review. Heliyon (2023)
Metadaten
Titel
Integrating Generative AI with TRIZ for Evolutionary Product Design
verfasst von
Marin Iuga
Stelian Brad
Copyright-Jahr
2025
DOI
https://doi.org/10.1007/978-3-031-75919-2_3