# A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in an Agri-Food Company
## 1 Introduction  
In this paper, we propose an innovative approach to overcome the main shortcomings of the traditional Failure Mode and Effect Analysis (FMEA) through a dynamic hybrid method and improve the system's reliability. Reliability is the probability that a component or a system will perform its intended functions over a specified period under stated operating conditions [1]. In recent decades, organisations and research companies have developed methods to mitigate or eliminate sudden events that would reduce a system's reliability. Engineers, to ensure system safety and reliability, identify all possible failures during the life cycle of a system and prepare actions to reduce failures or mitigate their consequences. In this regard, FMEA is a structured method used to identify failure modes and their consequences [2]. It becomes quantitative adding criticality analysis called FMECA (Failure Mode Effects and Criticality Analysis) [3]. A complete FMEA analysis consists of four steps [4; 5; 6]:  
1. Identify all failure modes that have occurred or the potential failure of a system;
2. Identify the causes and effects of faults;
3. Rank the identified failure modes through Risk Priority Number (RPN);
4. Take corrective action. In the third step, the RPN makes the FMEA a quantitative method. In order to carry out a correct FMEA analysis and identify all possible failure modes, a diversified team of people with different backgrounds (e.g., mechanical design, software, production, maintenance) is usually involved, as this increases the probability that all failures will be identified and the effects correctly estimated [3]. The Risk Priority Number (RPN) index is the product of three factors [7]:  
* Occurrence (O) is the probability that a failure mode will occur. It is, therefore, strongly linked to the failure rate of the component;* Severity (S) is related to the effect/impact of the fault model;
* Detectability (D) indicates the ability to diagnose the fault mode before its effects occur on the system. \[\text{RPN}=\text{O}*\text{S}*\text{D} \tag{1}\]  
The conventional method of the RPN calculation has been widely analysed in the literature for several reasons. Through these methods, three factors, namely the fault Occurrence (O), the Severity (S), and the Detectability (D), are evaluated, generally by using a scale of 10 values. Then, the FMECA provides, after calculating the RPN obtained by multiplying the value assigned to each factor, the ranking of causes to identify the most critical failure causes. On these causes, the corrective actions to be implemented and evaluated must be sought. Although the FMEA is a widespread analysis, its shortcomings are many and, in the literature, such as [PERSON], [PERSON], etc., the most cited shortcomings concern the absence of weights on O, S, and D factors, the absence of economic factors, the absence of scientific basis in the RPN calculation formula, and many duplicates in RPN results. The FMEA is an analysis led by a team to identify the possible failure modes in a system and the causes and effects associated with them. When unacceptable failure effects are verified, design changes must be made to either eliminate or reduce the failure causes. The first uses of FMEA as a structured methodology can be found in the United States' Department of Defense and applied by the National Air Force and Space Administration (NASA) for the Apollo plan to improve the system's reliability in the 1960s. Thanks to its ease of use, FMEA has been successfully applied in various industries such as aerospace and automotive, etc., [8]. The main weaknesses of the methodology concern the subjectivity inherent in the attribution of values to the three indexes of S, O, D, which have the same weight on the criticality of the single type of fault and the lack of decision support taking into account the costs of components.