Freight and Lead Time Estimator
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/164772 |
Resumo: | Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Freight and Lead Time EstimatorMachine learningData SciencePredictive modellingSupply chain managementLogisticsDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe role of supply chain and logistics systems in today's global economy is vital, and accurate prediction of Freight Cost and Lead Time (FLT) is crucial for ensuring the effectiveness and efficiency of production planning and scheduling. Traditional FLT prediction methods are typically inadequate, as they rely on historical averages that fail to account for the variable nature of modern supply chains. This project introduces, from scratch, innovative predictive models through an industrial case study that leverage Machine Learning algorithms to consider key factors such as volume, weight, and destination. The new tools were created to fix past weaknesses in the company's approach to logistics, aiming to greatly improve how clients feel about the service. They provide a strong improvement by introducing prediction abilities that didn't exist before, helping the company become more agile and focused on client needs.Henriques, Roberto André PereiraRUNTonoyan, Lilit2024-03-12T17:02:23Z2024-02-012024-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164772TID:203544072enginfo: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:RCAAP2024-03-18T01:45:12Zoai:run.unl.pt:10362/164772Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:01:59.727431Repositório 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 |
Freight and Lead Time Estimator |
title |
Freight and Lead Time Estimator |
spellingShingle |
Freight and Lead Time Estimator Tonoyan, Lilit Machine learning Data Science Predictive modelling Supply chain management Logistics Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
title_short |
Freight and Lead Time Estimator |
title_full |
Freight and Lead Time Estimator |
title_fullStr |
Freight and Lead Time Estimator |
title_full_unstemmed |
Freight and Lead Time Estimator |
title_sort |
Freight and Lead Time Estimator |
author |
Tonoyan, Lilit |
author_facet |
Tonoyan, Lilit |
author_role |
author |
dc.contributor.none.fl_str_mv |
Henriques, Roberto André Pereira RUN |
dc.contributor.author.fl_str_mv |
Tonoyan, Lilit |
dc.subject.por.fl_str_mv |
Machine learning Data Science Predictive modelling Supply chain management Logistics Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
topic |
Machine learning Data Science Predictive modelling Supply chain management Logistics Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
description |
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-03-12T17:02:23Z 2024-02-01 2024-02-01T00:00:00Z |
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/10362/164772 TID:203544072 |
url |
http://hdl.handle.net/10362/164772 |
identifier_str_mv |
TID:203544072 |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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 |
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1799138192591421440 |