Ítem
Solo Metadatos

Clustering Techniques

dc.creatorCruz, Antonio Miguelspa
dc.creatorUsaquén Perilla, Sandra Patriciaspa
dc.creatorVanegas Pabón, Nidia Nellyspa
dc.date.accessioned2020-08-06T16:21:41Z
dc.date.available2020-08-06T16:21:41Z
dc.date.created2010-03spa
dc.description.abstractThis paper investigates the use of clustering technique to characterize the providers of maintenance services in a health-care institution according to their performance. A characterization of the inventory of equipment from seven pilot areas was carried out first (including 264 medical devices). The characterization study concluded that the inventory on a whole is old [exploitation time (ET)/useful life (UL) average is 0.78] and has high maintenance service costs relative to the original cost of acquisition (service cost /acquisition cost average 8.61%). A monitoring of the performance of maintenance service providers was then conducted. The variables monitored were response time (RT), service time (ST), availability, and turnaround time (TAT). Finally, the study grouped maintenance service providers into clusters according to performance. The study grouped maintenance service providers into the following clusters. Cluster 0: Identified with the best performance, the lowest values of TAT, RT, and ST, with an average TAT value of 1.46 days; Clusters 1 and 2: Identified with the poorest performance, highest values of TAT, RT, and ST, and an average TAT value of 9.79 days; and Cluster 3: Identified by medium-quality performance, intermediate values of TAT, RT, and ST, and an average TAT value of 2.56 dayseng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/memb.2009.935708
dc.identifier.issnISSN: 2154-2287
dc.identifier.issnEISSN: 2154-2317
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/26428
dc.language.isoengspa
dc.publisherJournal & Magazinesspa
dc.relation.citationEndPage126
dc.relation.citationIssueNo. 2
dc.relation.citationStartPage119
dc.relation.citationTitleIEEE Pulse
dc.relation.citationVolumeVol. 29
dc.relation.ispartofIEEE Pulse, ISSN: 2154-2287 ; EISSN: 2154-2317, Vol.29, No.2 (2010-03); pp.119-126spa
dc.relation.urihttps://ieeexplore.ieee.org/document/5431943/authors#authorsspa
dc.rights.accesRightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.accesoRestringido (Acceso a grupos específicos)spa
dc.sourceIEEE Pulsespa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.keywordCluster Analysisspa
dc.subject.keywordContract Servicesspa
dc.subject.keywordData Interpretationspa
dc.subject.keywordStatisticalspa
dc.subject.keywordInternationalityspa
dc.subject.keywordMaintenancespa
dc.titleClustering Techniquesspa
dc.title.TranslatedTitleTécnicas de agrupamientospa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
Archivos
Colecciones