Volume 2 Issue 2 pp. 431-438 Spring, 2011


An artificial neural network model for optimization of finished goods inventory


Sanjoy K. Paul, Abdullahil Azaeem


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.


DOI: 10.5267/j.ijiec.2010.03.002

Keywords: Finished goods inventory, Artificial neural network, Optimization, Inventory model, Lot sizing
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