Data mining software for smaller distances between suppliers of auto parts companies and automakers and vehicle maintenance Davi Silvestre Moreira dos Reis, Dorotéa Vilanova Garcia

Abstract

The choice of raw material supplier is a very important decision in the day to day business. This is because the amount spent on the acquisition of products or services for the production of a good can vary from 50 to 80% of total gross revenues. Among the relevant factors in choosing a supplier of raw materials, distance is one of the most prevalent. This work presents an intelligent system that was developed specifically to find the smallest geographic distances in the automotive industry, specifically between car assemblers and manufacturers of auto parts and equipment. The initial data of the companies were in pure text-file format, under which, inert, important information to assist in the decision-making process on the choice of suppliers. The only factor considered in the choice of production partners, in this case, was the smallest geographic distance. To obtain the shortest distances, an intelligent system has been developed that uses the bases of the KDD (Knowledge Discovery In Databases) process, especially Data Mining. The initial data underwent a process of cleaning, debugging, quantity reduction and preparation, until it is possible to apply data mining techniques, in order to demonstrate the ability of the tool developed in extracting rich information to assist in the decision making process. Potential suppliers, based on the shortest distance between companies.