# A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in an Agri-Food Company
## 1 Introduction
In order to overcome the criticality of the single failure, the criticality analysis that turns FMEA into FMECA is introduced to prioritise failures based on the likelihood of the item failure mode and the severity of its impacts. To calculate each factor, i.e., O, S, and D, some researchers have allocated them the same importance [7; 9; 10]. However, the change in these values can produce the same RPN value even if they hide a different risk [11]. Understanding the information in an FMEA/FMECA analysis is insufficient to assess the three factors with certainty and directness ([PERSON] et al., 2002). The mathematical formulation of the RPN has no scientific basis, and ignores the importance of corrective actions and is calculated only from a risk point of view [12; 13]. Furthermore, the mathematical form adopted by RPN is susceptible to variations in the valuation of individual factors. The same RPN values can be generated from different O, S, and D values for up to 24 combinations for the same RPN [14]. According to the papers cited, it is clear that the interdependencies among the various error modes and the effects are not considered, and the RPN considers only three safety-oriented factors, not weighted and ranked, altogether leaving out economic factors. Therefore, the FMEA/FMECA has been combined with many techniques to reduce/eliminate the observed weaknesses to overcome the aforementioned shortcomings. The methods used in the literature are divided into five main categories, which are Multi-Criteria Decision Making (MCDM), Mathematical Programming (MP), Artificial Intelligence (AI), hybrid approaches, and others [15; 16]. MCDM techniques are the most frequently used category. FMEA/FMECA analysis can be seen as an MCDM problem due to multiple risk factors in assessing and prioritising failure modes. In this regard, in the literature, several methods are combined with FMEA/FMECA. [PERSON] developed a multi-attribute analysis called Multi-Attribute Failure Modes Analysis (MAFMA), using the Analytic Hierarchic Process (AHP) to solve the shortcomings of the traditional FMEA related to the RPN. The expected cost of the intervention is also considered [17; 18; 19]. Other studies combine the FMECA analysis method with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [20] to overcome some conventional FMECA, proposing a new way to calculate the RPN based on the TOPSIS method with fuzzy logic. The fuzzy logic has been used several times, and, in particular [21; 22; 23],[PERSON] et al. discussed a technique for combining interval type-2 fuzzy sets. This approach produces an optimisation model based on linguistic terms that are challenging to quantify; moreover, the model is not dynamic in time. Similarly, the uses of the TOPSIS method are not limited in real cases, and can be applied in a food sector company to reduce and stabilise maintenance costs. In 2017, it was used to monitor the possible failure of a submarine control module [24]. The combining use of the Preference Organization Ranking Model PROMETHEE and FMEA, proposed by [25], was to establish a multi-criterion group decision support system designed to classify failure modes into priority classes in the FMEA. In addition to PROMETHEE, the Group Decision Support Systems (GDSSs) were designed for sorting faults. One study [26] combined the FMEA and the VIseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) method to rank the indices and then classified the failure modes. Analysis in the literature clearly shows that the RPN calculation is critical, and several different approaches have been proposed to overcome this limit. The BWM has often been used in conjunction with other methods, such as with VIKOR [27] or with grey theory [28]. Furthermore [29] et al., in 2019, proposed an MCDM approach using an expert team to measure the weight of the identified criteria and alternatives to extend the BWM.