VOICE OF THE CUSTOMER MEETS THE MANUAL: BUILDING QFD WITH DATA-DRIVEN INTELLIGENCE

Abstract

Quality Function Deployment (QFD) is a customer-oriented product development methodology that translates customer requirements into engineering characteristics for designing high-quality products and services. Traditional QFD relies heavily on expert judgment, manual evaluation, and subjective decisionmaking, which often lead to inconsistencies, increased development time, and limited adaptability to rapidly changing customer expectations. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, and Natural Language Processing (NLP) have introduced data-driven approaches that enhance the efficiency and accuracy of Quality Function Deployment. This paper proposes a datadriven intelligent QFD framework that integrates customer feedback analytics, sentiment analysis, machine learning, and predictive modeling to automatically capture the Voice of the Customer (VoC) and transform it into engineering design requirements. The proposed framework combines structured and unstructured customer data collected from surveys, product reviews, social media platforms, and online feedback systems to generate intelligent House of Quality (HoQ) matrices. Comparative evaluation demonstrates that the proposed approach significantly improves customer requirement prioritization, decision-making accuracy, product quality, development efficiency, and customer satisfaction compared with conventional manual QFD techniques. Furthermore, the framework enables continuous learning from evolving customer preferences through intelligent analytics and automated recommendation mechanisms. The proposed research contributes to modern quality engineering by bridging traditional Quality Function Deployment methodologies with AI-powered data intelligence, thereby supporting customer-centric product innovation, strategic decision-making, and sustainable competitive advantage in intelligent manufacturing and service industries.