Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing
dc.contributor.author | O'Donovan, Peter | |
dc.contributor.author | Bruton, Ken | |
dc.contributor.author | O'Sullivan, Dominic T. J. | |
dc.contributor.funder | DePuy Synthes Spine | |
dc.contributor.funder | Irish Research Council | |
dc.contributor.funder | Enterprise Ireland | |
dc.date.accessioned | 2018-02-06T13:36:29Z | |
dc.date.available | 2018-02-06T13:36:29Z | |
dc.date.issued | 2016 | |
dc.description.abstract | Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a largescale Air Handling Unit (AHU). | en |
dc.description.sponsorship | Irish Research Council/ Enterpise Ireland (EPSPG/2013/578) | en |
dc.description.status | Peer reviewed | en |
dc.description.version | Published Version | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.articleid | 26 | |
dc.identifier.citation | O’Donovan, P., Bruton, K. and O’Sullivan, D. T. (2016) 'Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing', International Journal of Prognostics and Health Management, 7,026, (22pp). | en |
dc.identifier.endpage | 22 | |
dc.identifier.issn | 2153-2648 | |
dc.identifier.journaltitle | International Journal of Prognostics and Health Management | en |
dc.identifier.startpage | 1 | |
dc.identifier.uri | https://hdl.handle.net/10468/5397 | |
dc.identifier.volume | 7 | |
dc.language.iso | en | en |
dc.publisher | PHM Society | en |
dc.relation.uri | http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2016/ijphm_16_026.pdf | |
dc.rights | © 2016, Peter O’Donovan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | |
dc.subject | Industrial analytics methodology | en |
dc.subject | Smart manufacturing | en |
dc.title | Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing | en |
dc.type | Article (peer-reviewed) | en |
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