## 6 Conclusions  
The integration of PCA with AHP-QFD offers a robust approach to improving decision-making processes and feature prioritization across different industries. This study evaluated the applicability of PCA-AHP-QFD integration in the logistics and personal/consumer electronics industries, considering their unique challenges and potential benefits.  
In the logistics industry, PCA-AHP-QFD proved highly effective in simplifying complex AHP-QFD matrices and enhancing system resilience by optimizing logistics service centers. Expert feedback indicated a preference for this method due to its clarity, speed, and accuracy, supporting sustainable logistics practices and streamlined decision-making.  
For the personal/consumer electronics industry, the integration of PCA with AHP-QFD facilitated efficient decision-making, reduced development cycle time, and improved alignment with customer needs. Expert feedback highlighted significant improvements in evaluation speed and clarity, making this methodology suitable for rapidly evolving markets with high product demands.  
In the healthcare/medical industry, the PCA-AHP-QFD model showed limitations. While it helped streamline feature prioritization and manage design complexity, the distribution of variance in the datasets indicated that PCA might not be as beneficial in this sector. This underscores the importance of considering dataset characteristics when applying PCA.  
Expert feedback from various fields provided valuable insights into the practical application of PCA-AHP-QFD across different industries. There were both convergences and divergences in opinions. These diverse perspectives underscore the importance of tailoring the PCA-AHP-QFD methodology to specific industry needs and project phases, ensuring comprehensive and effective decision-making throughout the product development lifecycle.  
Expert feedback was gathered for the logistics and personal/consumer electronics industries, revealing insights into the strengths and limitations of the PCA-AHP-QFD approach. Experts from various fields generally found PCA-AHP-QFD beneficial in improving evaluation speed and clarity, particularly in the early design phases. However, for phases requiring intricate detail, such as prototype development or testing, complementary methods were suggested to ensure comprehensive decision-making.  
In summary, the PCA-AHP-QFD methodology demonstrates significant potential in enhancing feature prioritization and decision-making efficiency in the logistics and personal/consumer electronics industries. However, the healthcare/medical industry may require supplementary methods to address its specific challenges. Future research should focus on integrating complementary methods to further enhance the applicability of PCA-AHP-QFD in complex product design scenarios across different sectors.  
The practical implications for industry practitioners are profound. Companies can leverage PCA-AHP-QFD to streamline their product design processes, particularly where feature importance is concentrated and rapid decision-making is crucial. Key steps for implementation include understanding the methodology, training the team, collecting and preparing high-quality data, applying PCA to identify key variables, integrating with AHP-QFD for prioritization, and continuously monitoring and iterating the design based on feedback. This integrated approach not only enhances feature selection efficiency and alignment with customer needs but also drives innovation and competitiveness in the market.