Spares inventory prediction using back propagation neural networks

a case study

Abstract

Spares inventory is the key function in maintenance management and is a great challenge to estimate the current quantity with cost effectiveness. Modern manufacturing management invokes artificial intelligent techniques to enable faster and accurate decision making. This paper presents a novel method for machine spares inventory prediction using back propagation neural network. Traditional techniques have shown inadequacy in handling large data and limits the spares inventory prediction. This paper details on a back propagation neural network based spares inventory prediction for welding system. The results of the proposed system show that the developed model will be worth implementing in industries for quick breakdown resolution and for financial savings.