An artificial neural network model for optimization of finished goods inventory

摘要

In this paper, an artificial neural network (ANN) model is developed to determine the optimumlevel of finished goods inventory as a function of product demand, setup, holding, and materialcosts. The model selects a feed-forward back-propagation ANN with four inputs, ten hiddenneurons and one output as the optimum network. The model is tested with a manufacturingindustry data and the results indicate that the model can be used to forecast finished goodsinventory level in response to the model parameters. Overall, the model can be applied foroptimization of finished goods inventory for any manufacturing enterprise in a competitivebusiness environment.