Process Mining Applied to Postal Distribution

Autores
Martínez, Víctor; Lanzarini, Laura Cristina; Ronchetti, Franco
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Process mining is a technique that allows analyzing business processes through event logs. In this article, different process mining techniques are used to analyze data based on the postal distribution of products in the Argentine Republic between the years 2017 and 2019. The results obtained allow stating that 85% of the shipments made conform exactly to the model. The analysis of the situations with a low level of adjustment to the discovered process constituted a tool for quick identification of some recurring problems in the distribution, facilitating the analysis of the deviations that occurred. In the future, we expect to incorporate these techniques to build early notifications that warn about the existence of excessive deviations from the process.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Process Mining
Data mining
Postal Distribution
Postal Processes
Business process management
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/130342
Acceder
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network_name_str SEDICI (UNLP)
spelling Process Mining Applied to Postal DistributionMartínez, VíctorLanzarini, Laura CristinaRonchetti, FrancoCiencias InformáticasProcess MiningData miningPostal DistributionPostal ProcessesBusiness process managementProcess mining is a technique that allows analyzing business processes through event logs. In this article, different process mining techniques are used to analyze data based on the postal distribution of products in the Argentine Republic between the years 2017 and 2019. The results obtained allow stating that 85% of the shipments made conform exactly to the model. The analysis of the situations with a low level of adjustment to the discovered process constituted a tool for quick identification of some recurring problems in the distribution, facilitating the analysis of the deviations that occurred. In the future, we expect to incorporate these techniques to build early notifications that warn about the existence of excessive deviations from the process.Workshop: WBDMD - Base de Datos y Minería de DatosRed de Universidades con Carreras en Informática2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf271-280http://sedici.unlp.edu.ar/handle/10915/130342enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4info:eu-repo/semantics/reference/hdl/10915/129809info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:32:47Zoai:sedici.unlp.edu.ar:10915/130342Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:32:47.943SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Process Mining Applied to Postal Distribution
title Process Mining Applied to Postal Distribution
spellingShingle Process Mining Applied to Postal Distribution
Martínez, Víctor
Ciencias Informáticas
Process Mining
Data mining
Postal Distribution
Postal Processes
Business process management
title_short Process Mining Applied to Postal Distribution
title_full Process Mining Applied to Postal Distribution
title_fullStr Process Mining Applied to Postal Distribution
title_full_unstemmed Process Mining Applied to Postal Distribution
title_sort Process Mining Applied to Postal Distribution
dc.creator.none.fl_str_mv Martínez, Víctor
Lanzarini, Laura Cristina
Ronchetti, Franco
author Martínez, Víctor
author_facet Martínez, Víctor
Lanzarini, Laura Cristina
Ronchetti, Franco
author_role author
author2 Lanzarini, Laura Cristina
Ronchetti, Franco
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Process Mining
Data mining
Postal Distribution
Postal Processes
Business process management
topic Ciencias Informáticas
Process Mining
Data mining
Postal Distribution
Postal Processes
Business process management
dc.description.none.fl_txt_mv Process mining is a technique that allows analyzing business processes through event logs. In this article, different process mining techniques are used to analyze data based on the postal distribution of products in the Argentine Republic between the years 2017 and 2019. The results obtained allow stating that 85% of the shipments made conform exactly to the model. The analysis of the situations with a low level of adjustment to the discovered process constituted a tool for quick identification of some recurring problems in the distribution, facilitating the analysis of the deviations that occurred. In the future, we expect to incorporate these techniques to build early notifications that warn about the existence of excessive deviations from the process.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
description Process mining is a technique that allows analyzing business processes through event logs. In this article, different process mining techniques are used to analyze data based on the postal distribution of products in the Argentine Republic between the years 2017 and 2019. The results obtained allow stating that 85% of the shipments made conform exactly to the model. The analysis of the situations with a low level of adjustment to the discovered process constituted a tool for quick identification of some recurring problems in the distribution, facilitating the analysis of the deviations that occurred. In the future, we expect to incorporate these techniques to build early notifications that warn about the existence of excessive deviations from the process.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/130342
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4
info:eu-repo/semantics/reference/hdl/10915/129809
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
271-280
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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