## 2 Literature Review
### Comparative Analysis of Key Strengths and Limitations of MCDM Methods Combined with QFD  
In Table 1, it is clearly demonstrated that the AHP-QFD method is a widely used and continuously applied MCDM approach. However, other MCDM methods are also being utilized and investigated to further refine QFD techniques. Thus, analyzing the key strengths and limitations of various MCDM methods employed in design decision-making can enhance our understanding and lead to improvements in these methodologies.  
Table 2 offers a comprehensive comparative analysis of the key strengths and limitations of these methods. By examining various MCDM techniques which usually combine with QFD, such as AHP, fuzzy AHP, and ANP, a more nuanced understanding of their effectiveness, challenges, and applicability across different contexts can be attained.  
Based on Table 2, it can be concluded that each process has its own strengths and limitations, which can be improved and tailored to the QFD approach depending on the context and objectives of the application.  
QFD is highly effective in translating customer requirements into engineering characteristics, making it widely used in new product development due to its systematic process, ease of implementation, and ability to enhance communication and reduce development cycles. However, its reliance on subjective judgments can introduce biases and vagueness, and its qualitative nature may pose challenges in categorizing customer needs, making it less suitable for certain industries. Additionally, maintaining and updating QFD matrices can be difficult, complicating the decision-making process.  
Among MCDM, it is evident that each method has distinct strengths and limitations that can be tailored and improved upon to suit the QFD approach, depending on the specific context and objectives. For this study, the aim is to address key challenges in product design decision-making.  
The primary advantage of AHP is its ability to provide a quantitative method to prioritize customer requirements and technical characteristics based on their relative importance. This method adds objectivity and rigor to the decision-making process, reducing the subjectivity and bias inherent in QFD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **QFD** & **AHP** & **Fuzzy Logic, Fuzzy QFD, Fuzzy** & **ANP** \\
**Reference** & **AHP-QFD** & **AHP-QFD** & **AHP-QFD** & **ANP-QFD** \\ \hline Strength & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & &  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **QFD** & **AHP** & **Fuzzy Logic, Fuzzy QFD, Fuzzy** & **ANP** \\
**Reference** & **QFD** & **AHP-QFD** & **AHP-QFD** & **ANP-QFD** \\ \hline Limitations & QFD & AHP & Fuzzy logic, fuzzy QFD, fuzzy & **ANP** \\  & & AHP-QFD & AHP-QFD & AHP-QFD \\ \hline \multirow{4}{*}{[45]} & This study does not provide information about this part. & AHP assumes independence among criteria and is limited in handling interdependence and feedback. & Fuzzy logic, Fuzzy QFD, Fuzzy & **ANP** \\  & & & & \\  & & & & \\  &  
Compared to other methods like ANP and fuzzy logic, ANP also handles complex decision-making processes [25; 26; 45; 47; 49]. It is also complex and time-consuming due to its consideration of interdependence among criteria and detailed pairwise comparisons [45; 47; 48] Fuzzy logic, on the other hand, deals well with uncertainty and imprecision [25; 45; 46; 47; 50], but also can introduce additional complexity and be difficult to use [25; 45; 47; 48]. These methods demand extensive knowledge and skills, and if the team lacks expertise, it can slow down the QFD analysis. AHP simplifies the process by breaking down complex decisions into a hierarchy of more easily comprehended sub-problems, each of which can be improve independently [25; 45; 46; 47]  
Thus, AHP-QFD is chosen for improvement due to its combined strengths and easier implementation compared to ANP and fuzzy logic, which, while having significant advantages, require advanced skills and are time-consuming to analyze and understand.