An artificial neural network model for optimization of finished goods inventory

Paul, SK; Azeem, A

An artificial neural network model for optimization of finished goods inventory

Paul, SK

Azeem, A

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Publication Type:: Journal Article
Citation:: International Journal of Industrial Engineering Computations, 2011, 2 (2), pp. 431 - 438
Issue Date:: 2011-04-01

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Field	Value	Language
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Azeem, A	en_US
dc.date.issued	2011-04-01	en_US
dc.identifier.citation	International Journal of Industrial Engineering Computations, 2011, 2 (2), pp. 431 - 438	en_US
dc.identifier.issn	1923-2926	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116719
dc.description.abstract	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. © 2011Growing Science Ltd. All rights reserved.	en_US
dc.relation.ispartof	International Journal of Industrial Engineering Computations	en_US
dc.relation.isbasedon	10.5267/j.ijiec.2011.01.005	en_US
dc.title	An artificial neural network model for optimization of finished goods inventory	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.for	099999 Engineering not elsewhere classified	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

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. © 2011Growing Science Ltd. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/116719