# Optimization of PFMEA Team Composition in the Automotive Industry Using the IPF-RADAR Approach
## 2 Literature Review
### RADAR Method  
As stated in the introductory chapter, the RADAR method was first introduced in the paper [17], while the RADAR II version was presented for the first time in the paper [18]. The fundamental characteristics of the method from a mathematical perspective are given in the paper [39], which provides a mathematical proof of the consistency of the rankings obtained using the RADAR and RADAR II methods through the presentation of their basic features. In the paper [17], the author applied the RADAR method to determine the priority of failure modes. In addition, a comparison was made between the results obtained using the RADAR method and those obtained using the TOPSIS and ARAS methods, as well as the basic Risk Priority Number (RPN), including weighted RPN values (considering the importance of risk factors). A comparative analysis using the WS ranking similarity coefficient [40] showed a moderate to high level of overlap with the rankings obtained by TOPSIS and ARAS. When compared with the RPN and weighted RPN approach, this overlap was almost complete. In the paper [41], the authors addressed a similar problem but used a combined Action Priority (AP) and RPN approach, introducing three additional criteria to determine the sequence for addressing failure modes. These additional criteria were the cost-effectiveness of mitigation actions, the time necessary to remedy the issue, and the impact on production process realization. To describe the uncertainty in this problem, pre-defined linguistic termswere used and modelled with triangular fuzzy numbers. In this way, the activities of the PFMEA team were optimized. A comparison of results obtained by applying RADAR and RADAR II with those from other MADM methods was performed in the paper [18]. The author proposed a new model for equipment selection in the automotive industry and compared the results of the RADAR and RADAR II methods with those of TOPSIS, COmplex PRoportional Assessment (COPRAS) [42], VIKOR, ARAS, and EDAS [43] (Evaluation based on Distance from Average Solution). According to the WS ranking similarity coefficient, the degree of agreement between RADAR and RADAR II was very high (sometimes even absolute). Only the RADAR II method showed a moderate level of similarity with the EDAS method. Thus, only small variations were observed, which largely depended on the nature of each method. In the paper presenting the mathematical foundations of the RADAR and RADAR II methods [39], a comparison of these two methods was performed using three numerical examples. Although the rankings were largely similar, there were cases where deviations occurred. The basic characteristic of the RADAR method is that it \"favours\" alternatives that are stable across multiple criteria. When an alternative is \"below average\" across several criteria, its chances of achieving a high rank are significantly reduced. Naturally, the ranking also strongly depends on the weights of the criteria themselves, and both RADAR and RADAR II tend to favour alternatives that are \"stable\" with respect to the more important criteria. All this indicates that both methods are suitable for risk and reliability analysis problems. However, the study showed that the basic RADAR method is somewhat more flexible than the RADAR II version. In other words, the basic RADAR method allows for slightly greater negative deviations of an alternative from the \"average\" values. Since this paper addresses a personnel selection problem, the RADAR method was chosen in order to select candidates who outperform others across multiple criteria. Moreover, it favours candidates who perform well with respect to the most important criteria while reducing the effect of a candidate's extreme strength in a single criterion in cases where they do not meet the expected standards in other areas.