Previsão de falta de materiais no contexto de gestão inteligente de inventário: uma aplicação de aprendizado desbalanceado

Abstract

Material backorder (or stockout) is a common supply chain problem, impacting the inventory system service level and effectiveness. Identifying materials with the highest chances of shortage prior its occurrence can present a high opportunity to improve the overall company’s performance. However, the complexity of this sort of problem is high, due to class imbalance between missing items and not missing ones in inventory, which can achieve proportions of 1 to 100. In this work, machine learning classifiers are investigated in order to fulfill this gap in literature. Specific metrics such as area under the Receiver Operator Characteristic and precision-recall curves, sampling techniques and ensemble learning are employed to this particular task. The proposed model was tested in two real case-studies, in which it was verified that the use of the tool may contribute with the improvemnet of the service level in the supply chain.