A neural approach to product demand forecasting

Mahbub, N; Paul, SK; Azeem, A

A neural approach to product demand forecasting

Mahbub, N Paul, SK

Azeem, A

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Publication Type:: Journal Article
Citation:: International Journal of Industrial and Systems Engineering, 2013, 15 (1), pp. 1 - 18
Issue Date:: 2013-08-14

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Field	Value	Language
dc.contributor.author	Mahbub, N	en_US
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Azeem, A	en_US
dc.date.issued	2013-08-14	en_US
dc.identifier.citation	International Journal of Industrial and Systems Engineering, 2013, 15 (1), pp. 1 - 18	en_US
dc.identifier.issn	1748-5037	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117176
dc.description.abstract	This paper develops an artificial neural network (ANN) model to forecast the optimum demand as a function of time of the year, festival period, promotional programmes, holidays, number of advertisements, cost of advertisements, number of workers and availability. The model selects a feed-forward back-propagation ANN with 13 hidden neurons in one hidden layer as the optimum network. The model is validated with a furniture product data of a renowned furniture company. The model has also been compared with a statistical linear model named Brown's double smoothing model which is normally used by furniture companies. It is observed that ANN model performs much better than the linear model. Overall, the proposed model can be applied for forecasting optimum demand level of furniture products in any furniture company within a competitive business environment. Copyright © 2013 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Industrial and Systems Engineering	en_US
dc.relation.isbasedon	10.1504/IJISE.2013.055508	en_US
dc.subject.classification	Operations Research	en_US
dc.title	A neural approach to product demand forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	15	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	150309 Logistics and Supply Chain Management	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	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

This paper develops an artificial neural network (ANN) model to forecast the optimum demand as a function of time of the year, festival period, promotional programmes, holidays, number of advertisements, cost of advertisements, number of workers and availability. The model selects a feed-forward back-propagation ANN with 13 hidden neurons in one hidden layer as the optimum network. The model is validated with a furniture product data of a renowned furniture company. The model has also been compared with a statistical linear model named Brown's double smoothing model which is normally used by furniture companies. It is observed that ANN model performs much better than the linear model. Overall, the proposed model can be applied for forecasting optimum demand level of furniture products in any furniture company within a competitive business environment. Copyright © 2013 Inderscience Enterprises Ltd.

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

http://hdl.handle.net/10453/117176