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Abstract: 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.
Keywords: Quality Function Deployment; Generative AI; Customer Reviews; Product Innovation; AI Framework
1 Introduction
In the era of rapid technological change and evolving customer expectations, many companies face increasing pressure to adapt quickly and effectively to maintain competitiveness in the global market (Schaller et al., 2022). Accurately identifying Customer Requirements (CRs) and incorporating them into product development processes is essential for delivering customer-centered innovations and achieving sustainable growth (Sudirjo, 2023). One widely adopted method for translating customer needs into product specifications is Quality Function Deployment (QFD). QFD enables the systematic collection of the Voice of the Customer (VOC), maps it to Engineering Characteristics (ECs), and organizes this information into a structured relationship matrix known as the House of Quality (HOQ) (Akao, 1972). This approach allows firms to prioritize customer demands and allocate resources efficiently during early product planning. However, traditional QFD processes and related tools, such as the Kano model, rely heavily on time-consuming and costly methods, including market surveys, interviews, and focus groups. These methods often suffer from subjectivity, limited scalability, and an inability to capture real-time market trends (Shen et al., 2022; Wang & Chen, 2020). As a result, there has been growing interest in using online customer reviews as a scalable, low-cost alternative data source. These reviews provide rich, real-time feedback directly from end-users, offering insights into product performance, satisfaction, and unmet needs. Recent advances in Artificial...