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