The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment

Detalhes bibliográficos
Autor(a) principal: Mutemi, Abed
Data de Publicação: 2023
Outros Autores: Bação, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151946
Resumo: Mutemi, A., Bação, F. (2023). The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach. Journal of Engineering, 2023. https://doi.org/10.1155/2023/8557487
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spelling The Discriminants of Long and Short Duration Failures in Fulfillment Sortation EquipmentA Machine Learning ApproachCivil and Structural EngineeringChemical Engineering(all)Mechanical EngineeringHardware and ArchitectureIndustrial and Manufacturing EngineeringElectrical and Electronic EngineeringSDG 3 - Good Health and Well-beingMutemi, A., Bação, F. (2023). The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach. Journal of Engineering, 2023. https://doi.org/10.1155/2023/8557487Due to the difficulties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfillment centers. The length of time that fulfillment center equipment failures last is particularly important because it affects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. The gradient boosting classifier (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. This model has since been applied successfully to automate the detection of long- and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfillment operations. Since its launch, this system has contributed to saving over $500 million in fulfillment expenses. It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNMutemi, AbedBação, Fernando2023-04-19T22:24:11Z2023-04-152023-04-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/151946eng2314-4912PURE: 58651705https://doi.org/10.1155/2023/8557487info: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:RCAAP2024-03-11T05:34:18Zoai:run.unl.pt:10362/151946Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:44.261782Repositório 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 The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
A Machine Learning Approach
title The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
spellingShingle The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
Mutemi, Abed
Civil and Structural Engineering
Chemical Engineering(all)
Mechanical Engineering
Hardware and Architecture
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
title_short The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
title_full The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
title_fullStr The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
title_full_unstemmed The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
title_sort The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
author Mutemi, Abed
author_facet Mutemi, Abed
Bação, Fernando
author_role author
author2 Bação, Fernando
author2_role author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Mutemi, Abed
Bação, Fernando
dc.subject.por.fl_str_mv Civil and Structural Engineering
Chemical Engineering(all)
Mechanical Engineering
Hardware and Architecture
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
topic Civil and Structural Engineering
Chemical Engineering(all)
Mechanical Engineering
Hardware and Architecture
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
description Mutemi, A., Bação, F. (2023). The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach. Journal of Engineering, 2023. https://doi.org/10.1155/2023/8557487
publishDate 2023
dc.date.none.fl_str_mv 2023-04-19T22:24:11Z
2023-04-15
2023-04-15T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/151946
url http://hdl.handle.net/10362/151946
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2314-4912
PURE: 58651705
https://doi.org/10.1155/2023/8557487
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10
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
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