Model for supply risk prioritization based on fuzzy techniques and fuzzy cognitive maps considering a consensus group decision making

Abstract

Supply Chain Risk Management (SCRM) is an issue that has received importance in the literature in recent years. In SCRM, supply risk assessment attracts the largest volume of research, where five major research opportunities have been identified: (1) adequately deal with the imprecision present in human assessments due to their uncertainties or lack of data, (2) using a suitable technique for the decision process to be in a group, (3) appropriately addressing compensation and non-compensation between criteria, and (4) estimating risks through cause and effect dependencies between them. To contemplate these opportunities, the present project aims to propose a decision-making model that combines a consensus reaching process with techniques based on fuzzy theory set to consider experts opinions during the group judgment process; Fuzzy Inference System (FIS), to find the degree of importance for each risk assessed in a compensatory and non-compensatory approach; and Fuzzy Cognitive Maps (FCM), to update the risks' importance based on the influence between them. The project also includes the computational modeling of the proposal and the implementation of a pilot application to prioritize the supply risks that have the greatest impact on the evaluation of decision makers. Thus, the advantages and limitations identified during the pilot application of the proposed model will be analyzed. This project is a complement of the Master degree project that is currently in process, following the same idea of the application of FIS and FCM for ranking the risks. However, on the current project it is propose to use a consensus reaching process technique to support the group decision making. Therefore, it is possible to compare the advantages and limitations of each approach. (AU)