Decision making methods for formulating the reserve scheme of spare parts based on deep neural network

Abstract

Determination of the variety and quantity of Spares is the primary step to guarantee the spares supply. Firstly, this paper analyzes the factors affecting the reserve scheme of Spares. Then by analyzing the inherent five attributes of Spares, two methods are proposed to determine the variety and quantity of Spares based on deep neural network. The first method ranks Spares according to their importance. A relatively simple deep neural network is used to analyze every attribute of the Spares in turn. The second method inputs all the attributes of Spares into a relatively complex deep neural network to make the decision. The experimental results show the advantages of the two methods in terms of efficiency and accuracy for formulating the reserve scheme of Spares.