## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study References** & **Approach** & **Key Benefits** & **Industrial/Area** \\ \hline \multirow{3}{*}{[23]} & AHP-QFD combined with zero-one goal Programming (ZOGP) binary model & Effective site selection for cargo logistics centers, managed budget constraints, demonstrated managerial decision-making effectiveness & \multirow{3}{*}{Cargo Logistics Centers} \\ \cline{2-2} \cline{4-4}  & AHP-QFD method & Enhances prioritization of customer requirements in new product development, leading to better design alternatives. & New Product Development \\ \hline \multirow{3}{*}{[25]} & QFD integrating Kano model and fuzzy AHP & Improves value in product-service systems (PSSs) by focusing on customer requirements and managing uncertainty. & \multirow{3}{*}{PSS} \\ \cline{2-2} \cline{4-4}  & QFD and AHP & Improved design quality in buildings, better selection of design criteria, increased customer satisfaction & \multirow{3}{*}{Building Design} \\ \cline{2-2} \cline{4-4}  & AHP-QFD & Enhanced student information system, alignment of improvements with institutional capabilities & \multirow{3}{*}{Educational Systems} \\ \hline \multirow{3}{*}{[28]} & AHP-QFD & Balanced cultural preservation with commercial value, innovative renovation method for traditional houses & Traditional Residential Renovation \\ \cline{1-1} \cline{2-2} \cline{4-4}  & & & \\ \hline \hline \end{tabular}
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Table 1: AHP-QFD approaches in product design decision-making. Therefore, this study focuses on combining AHP and QFD to improve product design decision-making, leveraging their individual strengths for a more structured and quantitative approach. However, the complexity of the AHP-QFD approach remains a significant challenge. Techniques for reducing complexity or dimensionality, such as principal component analysis (PCA) [31; 32], are incorporated to facilitate easier analysis and decision-making. From the literature review on the PCA-QFD approach, it is evident that while PCA-QFD has potential, it remains unsuitable for some applications. The methodology itself may be correct, but input data likely play a crucial role, suggesting that resolving data issues could improve its effectiveness. This consideration leads us to enhance the PCA-QFD approach by integrating AHP, motivating the study of the PCA-AHP-QFD methodology. The combined use of PCA and AHP offers significant advantages. PCA reduces data complexity, making it more manageable, while AHP provides a structured decision-making framework. Studies have highlighted the benefits of using PCA and AHP together to enhance decision-making accuracy and efficiency [33; 34]. By integrating QFD, AHP, and PCA, the strengths of each method are leveraged to comprehensively address the limitations of QFD. This integrated approach ensures that customer needs are prioritized objectively, critical components are identified efficiently, and data complexity is reduced, leading to a more effective and streamlined product development process. Although previous studies have shown the benefits of using PCA or AHP with QFD, there has been limited research on combining all three methods. This study aims to fill this gap by evaluating the applicability and benefits of this integrated approach. In particular, this study addresses several key challenges as follows: the increasing complexity of designs extending development times, the demand for advanced skills complicating management, the loss of tacit knowledge, the need for collaboration among stakeholders with differing goals, and achieving consensus in group decision-making. By incorporating PCA with MCDM methods like AHP and QFD, this study aims to simplify data analysis, enhance decision-making accuracy, and comprehensively address these challenges. Details about PCA will be provided later in the literature review, specifically in Section 2.