Big Data Analytics Based on Logistical Models

Descripción del Articulo

Over the past years, a change of feedback data in terms of quantity, quality, and timeliness could be observed in production. The generation of high resolution production feedback data enables producing companies to apply big data analytics in order to create competitive advantages. This paper descr...

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Detalles Bibliográficos
Autores: Nywlt, Johannes, Grigutsch, Michael
Formato: artículo
Fecha de Publicación:2015
Institución:Pontificia Universidad Católica del Perú
Repositorio:PUCP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.pucp.edu.pe:20.500.14657/194842
Enlace del recurso:https://repositorio.pucp.edu.pe/index/handle/123456789/194842
Nivel de acceso:acceso abierto
Materia:Analytics
Big data
Competitive advantage
Logistical models
https://purl.org/pe-repo/ocde/ford#5.02.04
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spelling Nywlt, JohannesGrigutsch, Michael2023-07-21T19:18:24Z2023-07-21T19:18:24Z2015https://repositorio.pucp.edu.pe/index/handle/123456789/194842Over the past years, a change of feedback data in terms of quantity, quality, and timeliness could be observed in production. The generation of high resolution production feedback data enables producing companies to apply big data analytics in order to create competitive advantages. This paper describes how logistical models can be used to conduct big data analytics. It will be explained how such logistic-oriented big data analyses can be applied to improve the logistical performance of producing companies. The results will be illustrated with the help of a best practice project.engPontificia Universidad Católica del Perú. CENTRUMPEurn:issn:1851-6599info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0Journal of CENTRUM Cathedra, Vol. 8, Issue 1reponame:PUCP-Institucionalinstname:Pontificia Universidad Católica del Perúinstacron:PUCPAnalyticsBig dataCompetitive advantageLogistical modelshttps://purl.org/pe-repo/ocde/ford#5.02.04Big Data Analytics Based on Logistical Modelsinfo:eu-repo/semantics/articleArtículoORIGINALJCC-8.1-105.pdfJCC-8.1-105.pdfTexto completoapplication/pdf201149https://repositorio.pucp.edu.pe/bitstreams/59b8e3a5-b297-4e23-906e-89d115d5988a/download21e285c9b43e2446bc78a39fd122a89bMD51trueAnonymousREADTHUMBNAILJCC-8.1-105.pdf.jpgJCC-8.1-105.pdf.jpgIM Thumbnailimage/jpeg34205https://repositorio.pucp.edu.pe/bitstreams/0ec494c3-b4a0-4b7a-8703-b15348834a7b/downloadee796d81379d41ad388b386d504e7369MD52falseAnonymousREADTEXTJCC-8.1-105.pdf.txtJCC-8.1-105.pdf.txtExtracted texttext/plain18397https://repositorio.pucp.edu.pe/bitstreams/2d2b204c-487a-4b28-98ed-68438bd6596f/downloadde62baf9b667f248d39b02ac171d546dMD53falseAnonymousREAD20.500.14657/194842oai:repositorio.pucp.edu.pe:20.500.14657/1948422025-04-11 09:58:19.54http://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.pucp.edu.peRepositorio Institucional de la PUCPrepositorio@pucp.pe
dc.title.en_US.fl_str_mv Big Data Analytics Based on Logistical Models
title Big Data Analytics Based on Logistical Models
spellingShingle Big Data Analytics Based on Logistical Models
Nywlt, Johannes
Analytics
Big data
Competitive advantage
Logistical models
https://purl.org/pe-repo/ocde/ford#5.02.04
title_short Big Data Analytics Based on Logistical Models
title_full Big Data Analytics Based on Logistical Models
title_fullStr Big Data Analytics Based on Logistical Models
title_full_unstemmed Big Data Analytics Based on Logistical Models
title_sort Big Data Analytics Based on Logistical Models
author Nywlt, Johannes
author_facet Nywlt, Johannes
Grigutsch, Michael
author_role author
author2 Grigutsch, Michael
author2_role author
dc.contributor.author.fl_str_mv Nywlt, Johannes
Grigutsch, Michael
dc.subject.en_US.fl_str_mv Analytics
Big data
Competitive advantage
Logistical models
topic Analytics
Big data
Competitive advantage
Logistical models
https://purl.org/pe-repo/ocde/ford#5.02.04
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.02.04
description Over the past years, a change of feedback data in terms of quantity, quality, and timeliness could be observed in production. The generation of high resolution production feedback data enables producing companies to apply big data analytics in order to create competitive advantages. This paper describes how logistical models can be used to conduct big data analytics. It will be explained how such logistic-oriented big data analyses can be applied to improve the logistical performance of producing companies. The results will be illustrated with the help of a best practice project.
publishDate 2015
dc.date.accessioned.none.fl_str_mv 2023-07-21T19:18:24Z
dc.date.available.none.fl_str_mv 2023-07-21T19:18:24Z
dc.date.issued.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.other.none.fl_str_mv Artículo
format article
dc.identifier.uri.none.fl_str_mv https://repositorio.pucp.edu.pe/index/handle/123456789/194842
url https://repositorio.pucp.edu.pe/index/handle/123456789/194842
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn:1851-6599
dc.rights.es_ES.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0
dc.publisher.none.fl_str_mv Pontificia Universidad Católica del Perú. CENTRUM
dc.publisher.country.none.fl_str_mv PE
publisher.none.fl_str_mv Pontificia Universidad Católica del Perú. CENTRUM
dc.source.es_ES.fl_str_mv Journal of CENTRUM Cathedra, Vol. 8, Issue 1
dc.source.none.fl_str_mv reponame:PUCP-Institucional
instname:Pontificia Universidad Católica del Perú
instacron:PUCP
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Big Data Analytics Based on Logistical Models
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