Forecasting of optimum raw material inventory level using artificial neural network

Ali, SM; Paul, SK; Azeem, A; Ahsan, K

Forecasting of optimum raw material inventory level using artificial neural network

Ali, SM Paul, SK

Azeem, A Ahsan, K

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Publication Type:: Journal Article
Citation:: International Journal of Operations and Quantitative Management, 2011, 17 (4), pp. 333 - 348
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Ali, SM	en_US
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Azeem, A	en_US
dc.contributor.author	Ahsan, K	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	International Journal of Operations and Quantitative Management, 2011, 17 (4), pp. 333 - 348	en_US
dc.identifier.issn	1082-1910	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116717
dc.description.abstract	This paper develops an artificial neural network (ANN) model to forecast the optimum level of raw materials inventory as a function of product demand, manufacturing lead-time, supplier reliability, material holding cost, and material cost. The model selects a feed-forward back-propagation ANN with twelve hidden neurons as the optimum network. We test the model with pharmaceutical company data. The results show that the model can be useful to forecast raw material inventory level in response to different parameters. We also compare the model with fuzzy inference system (FIS) and simple economic order quantity (EOQ). It can be seen that ANN model outperforms others. Overall, the model can be applied for forecasting of raw materials inventory for any manufacturing enterprise in a competitive business environment.	en_US
dc.relation.ispartof	International Journal of Operations and Quantitative Management	en_US
dc.title	Forecasting of optimum raw material inventory level using artificial neural network	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	17	en_US
utslib.for	1503 Business and Management	en_US
utslib.for	1505 Marketing	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

This paper develops an artificial neural network (ANN) model to forecast the optimum level of raw materials inventory as a function of product demand, manufacturing lead-time, supplier reliability, material holding cost, and material cost. The model selects a feed-forward back-propagation ANN with twelve hidden neurons as the optimum network. We test the model with pharmaceutical company data. The results show that the model can be useful to forecast raw material inventory level in response to different parameters. We also compare the model with fuzzy inference system (FIS) and simple economic order quantity (EOQ). It can be seen that ANN model outperforms others. Overall, the model can be applied for forecasting of raw materials inventory for any manufacturing enterprise in a competitive business environment.

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

http://hdl.handle.net/10453/116717