## 3 Materials and Methods
### Modified QFD Process  
This section introduces a modified QFD approach (Figure 5), designed to enhance the QFD analytic process's efficiency by overcoming the limitations inherent in its conventional model. The proposed QFD approach incorporates IT2F logic for the mathematical modeling of vague linguistic variables, while the EDAS method is adopted to determine the importance prioritization of quality attributes. The proposed QFD process includes the following steps:  
* Step 1: Defining the request attributes: WHAT (\(d_{i}\))
* Step 2: Assessing the relative priority of the request attributes (\(\overset{\approx}{\widetilde{w}}_{i}\))
* Step 3: Identifying potential responses to requests--quality attributes: HOW (\(t_{j}\))
* Step 4: Establishing individual IT2F relation matrices: HoQ (\(\overset{\approx}{\widetilde{X}}^{l}=\left(\overset{\approx}{\widetilde{x}}_{ ij}\right)_{m\times n}\)); deriving the aggregated IT2F HoQ matrix (\(\overset{\approx}{\widetilde{X}}=\left(\overset{\approx}{\widetilde{x}}_{ij} \right)_{m\times n}\))
* Step 5: Calculating quality attributes' importance weight (\(\overset{\approx}{\widetilde{y}}_{j}\)): \begin{tabular}{r l} \(\bullet\) & Step 5.1: Establishing average matrix (\(\mathcal{V}=\left[\begin{matrix}\widetilde{\widetilde{v}}_{i}\\ \end{matrix}\right]_{m\times 1}\)) \\ \(\bullet\) & Step 5.2: Calculating positive (\(\widetilde{\widetilde{p}}_{ij}\)) and negative (\(\widetilde{\widetilde{n}}_{ij}\)) distances from the average solution (\(\widetilde{\widetilde{s}}_{i}\)) \\ \(\bullet\) & Step 5.3: Calculating weighted sums of the positive (\(\widetilde{\widetilde{s}}_{p}\)) and negative (\(\widetilde{\widetilde{s}}_{nj}\)) distances from the average solution (\(\widetilde{\widetilde{v}}_{i}\)) \\ \(\bullet\) & Step 5.4: Normalizing values \(\widetilde{\widetilde{s}}_{j}\) and \(\widetilde{\widetilde{s}}_{nj}\) \\ \(\bullet\) & Step 5.5: Calculating quality attributes' relative weight (\(\widetilde{\widetilde{y}}_{j}\)) \\ \(\bullet\) & Step 6: Calculating quality attributes' relative weight considering autocorrelations (\(\widetilde{\widetilde{y}}_{j}^{*}\)) \\ \(\bullet\) & Step 7: Computing quality attributes' absolute weight \(t_{j}-(\widetilde{\widetilde{\gamma}}_{j}^{*})\) \\ \end{tabular}   
The detailed procedure of the proposed QFD approach is depicted below. Step 1: Defining the request attributes (WHAT). Let us assume that the QFD analytical process includes \(m\) request attributes denoted as \(d_{i}(i=1,2,\ldots,m)\)  
Step 2: Assessing the relative priority of the request attributes \(\widetilde{\widetilde{w}}_{i}(\widetilde{\widetilde{w}}_{1},\widetilde{ \widetilde{w}}_{2},\ldots,\widetilde{\widetilde{w}}_{m})\). These priorities represent the IT2F relative weights obtained by the previously introduced IT2 FAHP process. Step 3: Identifying the quality attributes (HOW). Let us assume that the QFD analytical process includes \(n\) quality attributes denoted as \(t_{j}(j=1,2,\ldots,n)\). Step 4: Assume that \(L\) experts \(e_{l}(l=1,2,\ldots,L)\) are participating to evaluate the relationship between the \(n\) quality attributes and the \(m\) request attributes. Let \(\widetilde{\widetilde{x}}_{ij}^{l}\) be the ratings provided by the \(l^{th}\) expert for the \(j^{th}\) attribute \(t\) with respect to the \(i^{th}\) requirement \(d\). The correlation ratings for all discovered \(d_{i}-t_{j}\) relations offered by \(l^{th}\) expert create an individual relation matrix \(\widetilde{\widetilde{X}}^{l}=(\widetilde{x}_{ij}^{l})_{m\times n}\). Here, \(\widetilde{x}_{ij}^{l}\) is an IT2 TrFN derived according to the linguistic characterization used to describe correlation ratings as presented in Table 2.  
Figure 5: IT2 FQFD evaluation.