The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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