Delays prediction using data mining techniques for supply chain risk management company

Detalhes bibliográficos
Autor(a) principal: Manai, Nedra
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/57155
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Delays prediction using data mining techniques for supply chain risk management companyRiskPredictive ModelForecastingSupply Chain Risk ManagemeTrabalho de projectoProject workProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceGlobalization makes competition in supply chain management more intense. Pressure on improving the efficiency, guarantee that goods arrive on time and reduce the cost of shipment became higher. Shipment passes through different continents and cultures, dispersed around the world and encounter different conditions and risks. These risks are unexpected events that might disrupt the flow of materials or the planned operations. It can be due to late delivery, inaccuracy in forecasting, natural disasters like hurricane and earthquake or sociocultural events like strike. An effective use of supply chain risk management methods which includes risk identification, risk assessment, risk mitigation, and risk control is important for the organization to survive. For that reason, I was part of a team in XXX organization who has a goal to develop a predictive model to predict shipment delays for company’s customers.Jesus, Frederico Miguel Campos Cruz Ribeiro deRUNManai, Nedra2019-01-11T20:50:34Z2019-01-082019-01-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/57155TID:202138615enginfo: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:RCAAP2024-03-11T04:27:33Zoai:run.unl.pt:10362/57155Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:02.330654Repositório 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 Delays prediction using data mining techniques for supply chain risk management company
title Delays prediction using data mining techniques for supply chain risk management company
spellingShingle Delays prediction using data mining techniques for supply chain risk management company
Manai, Nedra
Risk
Predictive Model
Forecasting
Supply Chain Risk Manageme
Trabalho de projecto
Project work
title_short Delays prediction using data mining techniques for supply chain risk management company
title_full Delays prediction using data mining techniques for supply chain risk management company
title_fullStr Delays prediction using data mining techniques for supply chain risk management company
title_full_unstemmed Delays prediction using data mining techniques for supply chain risk management company
title_sort Delays prediction using data mining techniques for supply chain risk management company
author Manai, Nedra
author_facet Manai, Nedra
author_role author
dc.contributor.none.fl_str_mv Jesus, Frederico Miguel Campos Cruz Ribeiro de
RUN
dc.contributor.author.fl_str_mv Manai, Nedra
dc.subject.por.fl_str_mv Risk
Predictive Model
Forecasting
Supply Chain Risk Manageme
Trabalho de projecto
Project work
topic Risk
Predictive Model
Forecasting
Supply Chain Risk Manageme
Trabalho de projecto
Project work
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2019
dc.date.none.fl_str_mv 2019-01-11T20:50:34Z
2019-01-08
2019-01-08T00:00:00Z
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/10362/57155
TID:202138615
url http://hdl.handle.net/10362/57155
identifier_str_mv TID:202138615
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.format.none.fl_str_mv application/pdf
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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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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