Integration of Principal Component Analysis with AHP-QFD for Improved Product Design Decision-Making

Abstract

The complexity of quality function deployment (QFD) matrices often hinders efficient decision-making in product design, leading to missed opportunities and extended development times. This study explores the integration of principal component analysis (PCA) with analytic hierarchy process-QFD (AHP-QFD) to address these challenges. PCA, a machine learning technique, was applied to QFD matrices from product design research to reduce complexity and enhance prioritization efficiency. The integrated method was tested with a product design team across various industries, including logistics, healthcare, and consumer electronics. The analysis demonstrated that PCA effectively reduced matrix complexity, optimizing feature prioritization. In the logistics sector, PCA explained 99.2% of the variance with the first five components, while in consumer electronics, it accounted for 86.9% with the first four components. However, PCA showed limitations in the healthcare sector due to evenly distributed variance among components. Expert feedback highlighted the practical benefits of the integrated approach: 75% of logistics experts and 62.5% of consumer electronics experts found the method clearer. For speed, 100% of logistics and 87.5% of consumer electronics experts preferred the method for quicker evaluations. For accuracy, 75% of logistics and 62.5% of consumer electronics experts deemed the method more accurate. Overall, the PCA-AHP-QFD method simplifies decision-making processes and reduces development time, particularly in industries where feature prioritization is crucial. These findings underscore the potential of the integrated approach to enhance product development efficiency and feature prioritization, with suitability varying based on industry characteristics.

Keywords

• AHP-QFD
• machine learning
• product design
• decision-making
• PCA