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dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorEL KIHEL, Yousra
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorZOUGGAR AMRANI, Anne
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorDUCQ, Yves
ORCID: 0000-0001-5144-5876
IDREF: 119003791
dc.contributor.authorAMEGOUZ, Driss
dc.contributor.authorLFAKIR, Ahmed
dc.date.accessioned2023-10-03T07:18:22Z
dc.date.available2023-10-03T07:18:22Z
dc.date.issued2023-01-13
dc.identifier.issn0951-192Xen_US
dc.identifier.urioai:crossref.org:10.1080/0951192x.2022.2162605
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/183859
dc.description.abstractIn the context of internationalization, the supply chain is becoming complex with a profusion of decisions to take. The modeling and measurement of supply chain (SC) performance has been widely addressed by researchers, however the arrival of new technologies in the era of industry 4.0 is changing the environment and implicitly impacting the Key Performance Indicators (KPI) for SC management. Although several models exist, none of them is specifically oriented for SC operations management considering the importance of KPI and inclusion of technologies of industry 4.0 concomitantly.This paper presents a research methodology targeting a reference model to grasp SC state with decisions identification called GRAILOG from which a set of KPI is built to support the different decisions. A methodology called PPTechIP is then described and demonstrated to lead and advise the company on the industry 4.0 transformation relevant to build reliable KPI. PPTechIP is based on a set of radars split into different decision levels and functions of the SC based on GRAILOG model. Potential of Progress is calculated and assist the manger in their decision making. PSA (French Car Manufacturer) embracing the era of industry4.0 was chosen to implement the model. The results, using the suggested methodology, provide several interesting insights in the control indicators of PSA. Big Data, Augmented reality and collaborative robots grasp great attentions from PSA and are judged as prior to continue the follow up and Cloud computing is judged as being an alert, carefulness to over investment has to be considered.
dc.language.isoENen_US
dc.sourcecrossref
dc.subjectSupply chain management
dc.subjectKey Performance Indicators
dc.subjectIndustry 4.0
dc.subjectTechnologies
dc.subjectAutomotive industry
dc.titleMethodology combining industry 4.0 technologies and KPI’s reliability for supply chain performance
dc.typeArticle de revueen_US
dc.identifier.doi10.1080/0951192x.2022.2162605en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.journalInternational Journal of Computer Integrated Manufacturingen_US
bordeaux.page1128-1152en_US
bordeaux.volume36en_US
bordeaux.issue8en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.teamPRODUCTIQUE-MEI
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.title=Methodology%20combining%20industry%204.0%20technologies%20and%20KPI%E2%80%99s%20reliability%20for%20supply%20chain%20performance&rft.atitle=Methodology%20combining%20industry%204.0%20technologies%20and%20KPI%E2%80%99s%20reliability%20for%20supply%20chain%20performance&rft.jtitle=International%20Journal%20of%20Computer%20Integrated%20Manufacturing&rft.date=2023-01-13&rft.volume=36&rft.issue=8&rft.spage=1128-1152&rft.epage=1128-1152&rft.eissn=0951-192X&rft.issn=0951-192X&rft.au=EL%20KIHEL,%20Yousra&ZOUGGAR%20AMRANI,%20Anne&DUCQ,%20Yves&AMEGOUZ,%20Driss&LFAKIR,%20Ahmed&rft.genre=article


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