Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting

Paul, SK; Azeem, A; Ghosh, AK

Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting

Paul, SK

Azeem, A Ghosh, AK

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Publication Type:: Journal Article
Citation:: International Journal of Business Information Systems, 2015, 18 (3), pp. 268 - 284
Issue Date:: 2015-01-01

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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.contributor.author	Ghosh, AK	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	International Journal of Business Information Systems, 2015, 18 (3), pp. 268 - 284	en_US
dc.identifier.issn	1746-0972	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119257
dc.description.abstract	Copyright © 2015 Inderscience Enterprises Ltd. Determining optimum level of inventory is very important for any organisation which depends on various factors. In this research, six main factors have been considered as input parameters and the inventory level has been considered as the single output for this inventory management problem. Price of raw material, demand of raw material, holding cost, setup cost, supplier's reliability and lead time are considered as input parameters. An adaptive neuro-fuzzy inference system (ANFIS) has been applied as the artificial intelligence technique for modelling the inventory problem. ANFIS results have been compared with results from another artificial intelligence technique, artificial neural network (ANN), to validate the output results. Performance of both methods has been shown regarding different error measures. Comparison clearly shows the superiority of ANFIS results over ANN results and thus makes ANFIS a better choice for inventory level forecasting.	en_US
dc.relation.ispartof	International Journal of Business Information Systems	en_US
dc.relation.isbasedon	10.1504/IJBIS.2015.068164	en_US
dc.subject.classification	Business & Management	en_US
dc.title	Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	18	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	1503 Business and 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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

Abstract:

Copyright © 2015 Inderscience Enterprises Ltd. Determining optimum level of inventory is very important for any organisation which depends on various factors. In this research, six main factors have been considered as input parameters and the inventory level has been considered as the single output for this inventory management problem. Price of raw material, demand of raw material, holding cost, setup cost, supplier's reliability and lead time are considered as input parameters. An adaptive neuro-fuzzy inference system (ANFIS) has been applied as the artificial intelligence technique for modelling the inventory problem. ANFIS results have been compared with results from another artificial intelligence technique, artificial neural network (ANN), to validate the output results. Performance of both methods has been shown regarding different error measures. Comparison clearly shows the superiority of ANFIS results over ANN results and thus makes ANFIS a better choice for inventory level forecasting.

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http://hdl.handle.net/10453/119257