Big Data Analytics Based on Logistical Models
dc.contributor.author | Nywlt, Johannes | |
dc.contributor.author | Grigutsch, Michael | |
dc.date.accessioned | 2023-07-21T19:18:24Z | |
dc.date.available | 2023-07-21T19:18:24Z | |
dc.date.issued | 2015 | |
dc.description.abstract | 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. | en_US |
dc.identifier.uri | https://repositorio.pucp.edu.pe/index/handle/123456789/194842 | |
dc.language.iso | eng | |
dc.publisher | Pontificia Universidad Católica del Perú. CENTRUM | |
dc.publisher.country | PE | |
dc.relation.ispartof | urn:issn:1851-6599 | |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0 | * |
dc.source | Journal of CENTRUM Cathedra, Vol. 8, Issue 1 | |
dc.subject | Analytics | en_US |
dc.subject | Big data | en_US |
dc.subject | Competitive advantage | en_US |
dc.subject | Logistical models | en_US |
dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#5.02.04 | |
dc.title | Big Data Analytics Based on Logistical Models | en_US |
dc.type | info:eu-repo/semantics/article | |
dc.type.other | Artículo |
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