Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables

Detalhes bibliográficos
Autor(a) principal: Chon, Kimchann
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/104798
Resumo: Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros
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spelling Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variablesdemand forecastingautomotive industryneural networkparsimonious variableARIMADocumentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeirosAccurate demand forecasting is compulsory for a first-tier supplier to determine an optimal amount of parts to produce in order to minimize safety stock after supplying to the manufacturer. Producing under an actual order will negatively impact relationships with the industry while overproducing will face unnecessary carrying costs. This study was to develop a nonlinear autoregressive exogenous network (NARX) model to predict part demands of a first-tier supplier and compare its forecasting performances with an autoregressive integrated moving average (ARIMA) model. A parsimonious set of external variables (provisional demand order and the number of non-working days) were considered in the NARX model. The time lags for each variable and demand for the previous period were determined by analyzing autocorrelation functions. The dataset was obtained from a first-tier supplier for a year and divided into 70% training, 15% validation, and 15% testing sets. The performance evaluation resulted in the root mean square error (RMSE) of the proposed model being better than an ARIMA model in both training (18%) and testing (15%) sets. The promising results of the proposed NARX model could be crucial for improving manufacturing planning to efficiently reduce carrying costs and prevent stock out.2021-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10316/104798http://hdl.handle.net/10316/104798engChon, Kimchanninfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-25T21:59:05Zoai:estudogeral.uc.pt:10316/104798Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:21:26.762609Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
title Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
spellingShingle Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
Chon, Kimchann
demand forecasting
automotive industry
neural network
parsimonious variable
ARIMA
title_short Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
title_full Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
title_fullStr Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
title_full_unstemmed Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
title_sort Demand Forecasting of a First-Tier Supplier in Automotive Industry Using Nonlinear Autoregressive Network with Parsimonious Variables
author Chon, Kimchann
author_facet Chon, Kimchann
author_role author
dc.contributor.author.fl_str_mv Chon, Kimchann
dc.subject.por.fl_str_mv demand forecasting
automotive industry
neural network
parsimonious variable
ARIMA
topic demand forecasting
automotive industry
neural network
parsimonious variable
ARIMA
description Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros
publishDate 2021
dc.date.none.fl_str_mv 2021-06
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/104798
http://hdl.handle.net/10316/104798
url http://hdl.handle.net/10316/104798
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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