# Selection of Production Reliability Indicators for Project Simulation Model
## 2 Literature Background
### Quality Function Deployment (QFD)  
The Quality Function Deployment (or it is known as the House of Quality) is a structured, flexible framework whose focal point is the customer [15]. This methodology was invented in the 1960s for a quick product development by [PERSON] [9]. The application of this method has spread all over the world. The method became applied in other fields of industry, such as Supply Chain Management [10], Strategic Management [11], education [12], and the list could be expanded. The aim of this method is to identify the most important customer requirements and technical features of a certain product or service, make priority lists from them, and estimate the correlations, with which positive quality can be maximized. This method is usually complemented with marketing tools (such as survey or interview), however, KANO or VOC models were mostly applied to avoid vagueness in the customer requirements [16; 17]. As a consequence, excellent products are being manufactured, which means value for the customer. The method can be described in a house-format, as can be seen in the following figure (Figure 1):  
When the importance list from the customer requirements and technical parameters are identified, a relationship matrix should be filled with the following values: \"0\" means no correlation between a parameter and a requirement, \"1\" is for slight correlation, \"3\" stands for a moderate correlation, while \"9\" represents a strong correlation. Besides this, the direction of the correlation is important [18]. The direction refers to direct or inverse proportional correlation. To determine which technical parameters a company should consider improving, the following equation is applied, which is known as Final Score (\(FS\)):  
\[FS_{i}=\sum C_{ij}*w_{j} \tag{5}\]  
where, \(C_{ij}\) is the correlation value of a technical parameter \(i\) for a requirement \(j\), \(w_{j}\) is the weight of the requirement \(j\). To improve the results of the QFD method, quantitative decision-making method can be applied to transform vague requirements into exact ones, such as AHP [18]. An extensive literature review has been published by [PERSON] et al. [19] in which the joint application of QFD and AHP methodology is examined with a literature evaluation method. The main conclusion of this paper is that this combination is the most applied one in the following fields: manufacturing, supply chain, higher education, strategy, service, sustainability, marketing, and energy. In another approach of the combination of AHP with different tools, an extensive literature review has been published by [PERSON] et al. [19] in which joint application of QFD and AHP methodology is examined with literature evaluation method. A main conclusion of this paper is that this combination is the most applied one in the following fields: manufacturing, supply chain, higher education, strategy, service, sustainability, marketing, and energy. Another approach of the combination of AHP with different tools are investigated as well, and it is stated that QFD is applicable not only for determining the relative weights, but it can also support to calculate the relationship's intensities among variables [20]. The AHP stands for the Analytic Hierarchy Process, which is a multi-criteria decision-making tool invented by [PERSON] [21]. One of the main advantages comes from the pairwise  
Figure 1: Quality Function Deployment. comparison, which makes differently scaled requirements comparable with one another. As far as the execution of such a method is concerned, it is entered in a matrix, its columns and rows represent the same elements: Therefore, the main diagonal of the matrix remains 1, but the matrix is mostly filled in with the following values, see [22]:  
1: Equal importance  
3: Moderate importance  
5: Strong importance  
7: Very strong importance  
9: Extreme importance  
2,4,6,8: Intermediate values, when compromise is needed.