AI-Driven Framework for Customer Requirement and Quality Analysis

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.