Freight and Lead Time Estimator

Detalhes bibliográficos
Autor(a) principal: Tonoyan, Lilit
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
id RCAP_949840eb2963ed958d98ef08ddfef913
oai_identifier_str oai:run.unl.pt:10362/164772
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
instname_str 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
repository.mail.fl_str_mv
_version_ 1799138192591421440

Registros relacionados