Toward managing demand variability by neuro-fuzzy approach

Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (...

Full description

Saved in:
Bibliographic Details
Main Authors: Wen-Pai Wang, Chun-Chih Chiu
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
ANFIS
Artificial neural networks
Data models
demand variability
Forecasting
Marketing and sales
Predictive models
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1692
container_issue
container_start_page 1688
container_title
container_volume
creator Wen-Pai Wang
Chun-Chih Chiu
description Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data, whereby to construct the needed forecasting model, hoping to make accurate forecasts according to the demand variability. Afterward the proposed forecasting model is compared with the conventional regression analysis and back-propagation network to verify its feasibility and validity.
doi_str_mv 10.1109/IEEM.2010.5674595
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_5674595</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5674595</ieee_id><sourcerecordid>5674595</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-546738740d191a539a0946c96632d2863dad9987513e29333f47ec43e7c19cf73</originalsourceid><addsrcrecordid>eNpVkM1Kw0AUhcc_sNQ8gLiZF0idO3d-ct1JiVqouMnCXZlmJnWkTULSKunTG7AIrs45fHA4HMZuQcwABN0v8vx1JsUYtbFKkz5jCdkMlFQq0wLhnE0kaJuike8X_xjYyz8GcM2Svv8UQoDMjCQzYQ9F8-06z3eudptYb7gPo_X8y3XRreM27ge-HngdDl2TVofjceCubbvGlR837Kpy2z4kJ52y4ikv5i_p8u15MX9cppHEPtXKWMysEh4InEZygpQpyRiUflyB3nmizGrAIAkRK2VDqTDYEqisLE7Z3W9tDCGs2i7uXDesTkfgDzh5S3I</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Toward managing demand variability by neuro-fuzzy approach</title><source>IEEE Xplore All Conference Series</source><creator>Wen-Pai Wang ; Chun-Chih Chiu</creator><creatorcontrib>Wen-Pai Wang ; Chun-Chih Chiu</creatorcontrib><description>Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data, whereby to construct the needed forecasting model, hoping to make accurate forecasts according to the demand variability. Afterward the proposed forecasting model is compared with the conventional regression analysis and back-propagation network to verify its feasibility and validity.</description><identifier>ISSN: 2157-3611</identifier><identifier>ISBN: 9781424485017</identifier><identifier>ISBN: 1424485010</identifier><identifier>EISSN: 2157-362X</identifier><identifier>EISBN: 9781424485031</identifier><identifier>EISBN: 9781424485024</identifier><identifier>EISBN: 1424485037</identifier><identifier>EISBN: 1424485029</identifier><identifier>DOI: 10.1109/IEEM.2010.5674595</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; ANFIS ; Artificial neural networks ; Data models ; demand variability ; Forecasting ; Marketing and sales ; Predictive models ; Training</subject><ispartof>2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010, p.1688-1692</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5674595$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5674595$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wen-Pai Wang</creatorcontrib><creatorcontrib>Chun-Chih Chiu</creatorcontrib><title>Toward managing demand variability by neuro-fuzzy approach</title><title>2010 IEEE International Conference on Industrial Engineering and Engineering Management</title><addtitle>IEEM</addtitle><description>Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data, whereby to construct the needed forecasting model, hoping to make accurate forecasts according to the demand variability. Afterward the proposed forecasting model is compared with the conventional regression analysis and back-propagation network to verify its feasibility and validity.</description><subject>Accuracy</subject><subject>ANFIS</subject><subject>Artificial neural networks</subject><subject>Data models</subject><subject>demand variability</subject><subject>Forecasting</subject><subject>Marketing and sales</subject><subject>Predictive models</subject><subject>Training</subject><issn>2157-3611</issn><issn>2157-362X</issn><isbn>9781424485017</isbn><isbn>1424485010</isbn><isbn>9781424485031</isbn><isbn>9781424485024</isbn><isbn>1424485037</isbn><isbn>1424485029</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVkM1Kw0AUhcc_sNQ8gLiZF0idO3d-ct1JiVqouMnCXZlmJnWkTULSKunTG7AIrs45fHA4HMZuQcwABN0v8vx1JsUYtbFKkz5jCdkMlFQq0wLhnE0kaJuike8X_xjYyz8GcM2Svv8UQoDMjCQzYQ9F8-06z3eudptYb7gPo_X8y3XRreM27ge-HngdDl2TVofjceCubbvGlR837Kpy2z4kJ52y4ikv5i_p8u15MX9cppHEPtXKWMysEh4InEZygpQpyRiUflyB3nmizGrAIAkRK2VDqTDYEqisLE7Z3W9tDCGs2i7uXDesTkfgDzh5S3I</recordid><startdate>201012</startdate><enddate>201012</enddate><creator>Wen-Pai Wang</creator><creator>Chun-Chih Chiu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201012</creationdate><title>Toward managing demand variability by neuro-fuzzy approach</title><author>Wen-Pai Wang ; Chun-Chih Chiu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-546738740d191a539a0946c96632d2863dad9987513e29333f47ec43e7c19cf73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accuracy</topic><topic>ANFIS</topic><topic>Artificial neural networks</topic><topic>Data models</topic><topic>demand variability</topic><topic>Forecasting</topic><topic>Marketing and sales</topic><topic>Predictive models</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Wen-Pai Wang</creatorcontrib><creatorcontrib>Chun-Chih Chiu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wen-Pai Wang</au><au>Chun-Chih Chiu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Toward managing demand variability by neuro-fuzzy approach</atitle><btitle>2010 IEEE International Conference on Industrial Engineering and Engineering Management</btitle><stitle>IEEM</stitle><date>2010-12</date><risdate>2010</risdate><spage>1688</spage><epage>1692</epage><pages>1688-1692</pages><issn>2157-3611</issn><eissn>2157-362X</eissn><isbn>9781424485017</isbn><isbn>1424485010</isbn><eisbn>9781424485031</eisbn><eisbn>9781424485024</eisbn><eisbn>1424485037</eisbn><eisbn>1424485029</eisbn><abstract>Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data, whereby to construct the needed forecasting model, hoping to make accurate forecasts according to the demand variability. Afterward the proposed forecasting model is compared with the conventional regression analysis and back-propagation network to verify its feasibility and validity.</abstract><pub>IEEE</pub><doi>10.1109/IEEM.2010.5674595</doi><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2157-3611
ispartof 2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010, p.1688-1692
issn 2157-3611
2157-362X
language eng
recordid cdi_ieee_primary_5674595
source IEEE Xplore All Conference Series
subjects Accuracy
ANFIS
Artificial neural networks
Data models
demand variability
Forecasting
Marketing and sales
Predictive models
Training
title Toward managing demand variability by neuro-fuzzy approach
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T11%3A34%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Toward%20managing%20demand%20variability%20by%20neuro-fuzzy%20approach&rft.btitle=2010%20IEEE%20International%20Conference%20on%20Industrial%20Engineering%20and%20Engineering%20Management&rft.au=Wen-Pai%20Wang&rft.date=2010-12&rft.spage=1688&rft.epage=1692&rft.pages=1688-1692&rft.issn=2157-3611&rft.eissn=2157-362X&rft.isbn=9781424485017&rft.isbn_list=1424485010&rft_id=info:doi/10.1109/IEEM.2010.5674595&rft.eisbn=9781424485031&rft.eisbn_list=9781424485024&rft.eisbn_list=1424485037&rft.eisbn_list=1424485029&rft_dat=%3Cieee_CHZPO%3E5674595%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-546738740d191a539a0946c96632d2863dad9987513e29333f47ec43e7c19cf73%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5674595&rfr_iscdi=true