Proposal of a supply risk classify method based on the application of fuzzy techniques for group decision making

Supply Chain Risk Management (SCRM) is an issue that has been widely discussed in academic studies and in organizations. 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) consider that risk factor with a high level of severity can not be compensated by a low occurrence level; (4) use a Fuzzy Rule-Based Classification System (FRBCS) for supply risk assessment; and (5) use real data for the application of risk management models in the supply chain. To contemplate these opportunities, the present project aims to propose a decision-making model that combines the hesitant fuzzy linguistic term set (HFLTS) to mathematically model the hesitation of the decision makers during the group decision making process (GDM) with the FRBCS to classify the risk factors in a compensatory and non-compensatory approach. The dissertation 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. The pilot application results show that there are coherent with the enterprise reality. The sensibility analysis confirms the non-compensatory behavior of the model. In addition, it was demonstrated that is possible to adjust the fuzzy numbers to obtain results to support the decision making process. The sensibility analysis also pointed that the experts hesitation have impact in the ranking and in the classification of the risk factors. (AU)