Fuzzy PIPRECIA based FMEA method for agricultural risk analysis application

Within the scope of the presented thesis, it is aimed to develop a model that includes the identification of possible failure modes during wheat and barley breeding studies, which are carried out in an organization operating on agricultural R&D studies and which have a critical role in ensuring the sustainability of agricultural production, and the determination of areas that may pose technical risks and risk priorities. In this regard, risks were analyzed separately with both classical FMEA (Failure Mode and Effect Analysis) and the proposed Fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) and FMEA integrated approach, and the effectiveness and performance of the integrated approach were measured by comparing the results obtained. In the proposed model, unlike the classical approach, FMEA team members were considered as criteria and members were evaluated with the Fuzzy PIPRECIA method according to their field experience and plant breeding information in line with the opinions of three decision makers and weight was assigned to each of them. The validity of the model was measured by considering Spearman and Pearson correlation coefficients. Then, these values were integrated into the points given by the FMEA team members, and a common input value was obtained for the probability, severity and detectability parameters by taking the geometric mean of these values, and RPN (Risk Priority Number) scores were calculated. To verify the model, sensitivity analysis was performed by changing the criterion weights. Finally, the applicability and effectiveness of the model were measured by comparing the proposed model results with the classical FMEA method results.