| dc.description.abstract | Taking into account the decision-making process of bidding, which there’re several strategic factors, complex and subjective, this document has as goal provide a new decision model, using a fuzzy system based on parameters, supporting the manager on binary decision-making of participating or not in a bidding. Beginning from a literature review about bidding, decision-making of bidding and fuzzy system, it approaches the new bidding decision-making, comparing to each others there are, as well as it application, here represented by a case study in a capital goods factory, with a strong national market performance. The obstruction to get information, the unstructured formulation of the decision-making process, the presence of limited resources, the increasing demands for product qualities and the possibility to help managers on process decision-making, motivated the document development. Taking into account that, during the whole information gathering process, it was verified the very qualitative variables presence, imprecise and the decision-making process based on human knowledge, whose behavior is influenced by perception, judgment and emotions, chose to develop a fuzzy model so, allowing the development of methods and techniques for representation and processing inaccurate information, abundant in the real world, and also difficult to process by conventional logic. The knowledge construction of the knowledge base of fuzzy systems, formed by database and by parameters was developed by the researcher, with specialists support. The expected benefit with the use of the new model is to get reduction of the manager's response time, more assertive decisions and saving of human and financial resources during the process of preparing proposals. With a historical database of a capital goods factory was possible to build up several bidding decision scenarios. Besides the model, a parameters fuzzy system was developed, whose results are in accordance with the decisions expected by those experts. The model, compared to the other models according to the literature review, was more robust, more interpretable and less dependent on the specialists. | |
