# New Possibilities of Using DEMATEL and ERPN in the New PFMEA Hybrid Model
## 1 Introduction  
Continuous improvement is an important global aspect of the growth and competitiveness of manufacturing organizations. Improving the quality of production implies the elimination of failure modes in the production process. For each single failure mode, it is important to know and research the causes of that failure [1]. FMEA (Failure Mode and Effect Analysis) is a tool used to identify failure modes of either a manufacturing process (PFMEA), design (DFMEA), system (SFMEA), or service [2]. It also has its applications in production quality and in solving the reliability of technical systems [3, 4]. The goal of a process FMEA is to improve the manufacturing process, ensure the product is of the required quality, minimize downtime, and minimize defective products and scrap. The focus of the PFMEA is on the manufacturing and assembly process [5]. The risk priority number (RPN) is used to determine the risk or criticality of failure modes. The RPN is obtained by simply multiplying the risk factors such as occurrence (O), severity (S), and detection (D). The problem is that this determination of the RPN can be highly subjective and introduces many shortcomings. This invalidates its full use in practice [6]. Many authors have been devoted to solving the problems of conventional PFMEA. Authors have proposed various alternative approaches to solve this problem. [PERSON] [2] proposed a new framework to improve the execution of the PFMEA while improving risk assessment. The new FMEA trends offer an opportunity to refine the results of the method. However, there is still a question of how important these parameters are to the size of the RPN and the resulting threats. Several authors consider the S and O factors as the two most important risk factors [7]. The limits of PFMEA can be explained as follows: we identified three failure modes, A, B and C, which we ranked as follows: (SA = 8, OA = 5, DA = 4), (SB = 10, OB = 4, DB = 4), and (SC = 5, OC = 4, DC = 8). Although each factor is rated differently, the resulting RPN is 160 for all failure modes. This poses a problem in determining the prioritization for the measures proposed. This is a metric where equal weight is given to each of the risk factors (S, O, and D) [8]. As summarized by [PERSON] [9] in his paper, many authors have proposed many ways to enhance the effectiveness of FMEA. The most widely used techniques are cognitive mapping [10]; mathematical programming methods such as data envelopment analysis (DEA) [11]; adaptive resonance theory [12]; linear programming [13]; fuzzy rule-base [14;15]. Often used are multi-criteria decision-making (MCDM) methods such as a technique for order preference by similarity to ideal solution (TOPSIS) [16; 17; 18]; grey relational analysis (GRA) [19;20]; and decision-making trial and evaluation laboratory (DEMATEL) [9;21;22]. Hybrid models have also found their use as a combination of the baseline method (PMEA) with fuzzy analytic hierarchy process (AHP) and fuzzy TOPSIS [23]; integrated approaches such as similarity measure and adaptive resonance theory [24], D numbers, and grey relational projection method [25]; extended FMEA [8]; PFDA-FMEA [26], and other methods and techniques [27;28]. The Battelle Memorial Institute developed the DEMATEL method to solve complex and interrelated problems [29;30]. The DEMATEL methodology is based on pairwise comparisons of individual factors, the definitions and descriptions of which will be available to experts to assess the interrelations between the factors (quantitatively using scales). Its strength is its ability to map all the influences and factors of a decision problem, including their representation in a clear influence map. In practice, it involves one or two rounds of questioning via a paper-based or online questionnaire. It is possible to include a larger number of experts, resulting in a map that depicts the relations among them as well as the significance of each step [31].