AI-Driven Framework for Customer Requirement and Quality Analysis
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| Опубликовано в:: | ISPIM Innovation Symposium (Jun 2025), p. 1-6 |
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| Главный автор: | |
| Другие авторы: | , |
| Опубликовано: |
The International Society for Professional Innovation Management (ISPIM)
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text Full Text - PDF |
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| 045 | 2 | |b d20250601 |b d20250630 | |
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| 100 | 1 | |a Seo, Sumin |u Korea Institute of Science and Technology Information (KISTI), 66, Heogi-ro, Dongdaemun-gu, Seoul 02456, Republic of Korea E-mail: syg3793@kisti.re.kr | |
| 245 | 1 | |a AI-Driven Framework for Customer Requirement and Quality Analysis | |
| 260 | |b The International Society for Professional Innovation Management (ISPIM) |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study proposes an AI-driven framework to automate the extraction and analysis of Customer Requirements (CRs) and Engineering Characteristics (ECs) from large-scale product review data. Traditional Quality Function Deployment (QFD) methods are labor-intensive, costly, and lack scalability and real-time responsiveness. To address these issues, the framework leverages recent advances in generative AI: instruction tuning improves task-specific comprehension, Retrieval-Augmented Generation (RAG) enhances contextual grounding, and prompt engineering ensures structured, actionable outputs. A domain-specific CR-EC dictionary aligns customer language with technical attributes, while instruction-response training improves model interpretability. The framework includes a scalable pipeline for data segmentation, inference, and post-processing. By enabling real-time demand sensing and reducing VOC collection costs, it supports agile product development and quality management. Future research will focus on validating the framework across domains and refining it based on empirical findings, contributing to AI-enabled, customer-driven innovation and automated product quality assessment. | |
| 653 | |a Language | ||
| 653 | |a Quality management | ||
| 653 | |a Dictionaries | ||
| 653 | |a Customer satisfaction | ||
| 653 | |a Usability | ||
| 653 | |a Deep learning | ||
| 653 | |a Trends | ||
| 653 | |a Customer feedback | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Prompt engineering | ||
| 653 | |a Automation | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Innovations | ||
| 653 | |a Customers | ||
| 653 | |a Quality assessment | ||
| 653 | |a Product development | ||
| 653 | |a Product quality | ||
| 653 | |a Costs | ||
| 653 | |a Product reviews | ||
| 653 | |a Engineering | ||
| 653 | |a Natural language processing | ||
| 653 | |a Real time | ||
| 653 | |a Quality function deployment | ||
| 700 | 1 | |a Byun, Jeongeun |u Korea Institute of Science and Technology Information (KISTI), 66, Heogi-ro, Dongdaemun-gu, Seoul 02456, Republic of Korea E-mail: jebyun@kisti.re.kr | |
| 700 | 1 | |a Bae, Kukjin |u Korea Institute of Science and Technology Information (KISTI), 66, Heogi-ro, Dongdaemun-gu, Seoul 02456, Republic of Korea E-mail: baekj@kisti.re.kr | |
| 773 | 0 | |t ISPIM Innovation Symposium |g (Jun 2025), p. 1-6 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3238450778/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3238450778/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3238450778/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |