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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/130342
Ver los metadatos del registro completo
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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:UNLP2025-09-03T11:04:53Zoai: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:13292025-09-03 11:04:53.866SEDICI (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 |
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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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conferenceObject |
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publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/130342 |
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http://sedici.unlp.edu.ar/handle/10915/130342 |
dc.language.none.fl_str_mv |
eng |
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eng |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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