Uso de machine learning para classificação de fornecedores no contexto da data science

Abstract

Decision-making for groups, public or private, is indispensable to the development of organizations, and searching for mechanisms to support the managers more assertively is fundamental to this goal. Know how to use raw data transforming them into knowledge allows these decisions to be based on data besides purely on intuition. Between the important decisions taken by any organization, the classification and selection of suppliers are an important practice to industrial engineering and Data Science is an ascendant field that studies data and how to realize this transformation of raw data into knowledge. To this research were used real data from suppliers of an enterprise of the aeronautical sector in its analyses. So, this research acted between Data Science and Classification and Selection of suppliers and had the focus on a problem known as clusterization that is the segmentation of data in regions as homogeneous as possible when there´s no existence of previous categories and aim to solve this problem supporting in the supplier´s management. This happens in practice using Data Science tools known as Machine Learning that are algorithms that can be used in the segmentation of groups without an initial classification. To the development has been used the procedure CRISP-DM that allows elucidate analyses´ problems helping to structure the scientific thinking. That way, by using this procedure, this dissertation had its general objetive in the use of the technique of Machine Learning to help in the classification and selection of suppliers of that organization. Having two specific objectives, the first one consisted of an analysis of those algorithms in the demonstration of the operation and behavior of the classic algorithms of clusterization of the real database. The second one consisted in analyzing those clusterization algorithms in search of the most appropriate to the supplier´s base culminating with the creation and suggestion of a framework that can be used for future clustering analises. The clustering modelings were realized and through internal and stability validations had their efficiency tested allowing the data to be split into clusters. The use of CRISP-DM allowed that the clustering framework was proposed