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Key performance indicators selection through an analytic network process model for tooling and die industry

2021-12-14Artigo científico Acesso aberto
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dc.contributor.authorRodrigues, Diogo
dc.contributor.authorGodina, Radu
dc.contributor.authorda Cruz, Pedro Espadinha
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2022-01-06T23:52:17Z
dc.date.available2022-01-06T23:52:17Z
dc.date.issued2021-12-14
dc.description
dc.description.abstractIn the last few decades, the fast technological development has caused high competitiveness among companies, encouraging a pursuit for strategies that allow them to gain competitive advantage, such as the monitoring of performance by using key performance indicators (KPIs). However, its selection process is complex since there are several KPIs available to evaluate performance and different relationships between them. To overcome this challenge, the use of a multiple criteria decision-making model (MCDM) was proposed, namely the analytic network process (ANP) through which a reduced number of them are prioritized. To identify which KPIs are suitable for the press cast and die manufacturing industry, a literature review was made, and 58 unique KPIs were identified. Thus, to validate the proposed methodology, a case study was carried out in an automotive press molding industry. With the implementation of the proposed ANP model it was possible to identify 9 KPIs that ensure the correct molding process monitoring, while being aligned with the Balanced Scorecard criteria. The results show that the proposed model is suitable for selecting KPIs for the molding industry.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent1109602
dc.identifier.doi10.3390/su132413777
dc.identifier.issn2071-1050
dc.identifier.otherPURE: 35593030
dc.identifier.otherPURE UUID: 0ac953bb-c5fe-4329-9561-4e92ef2db815
dc.identifier.otherScopus: 85121269670
dc.identifier.otherORCID: /0000-0002-5337-0633/work/105859137
dc.identifier.otherORCID: /0000-0003-1244-5624/work/205572254
dc.identifier.urihttp://hdl.handle.net/10362/130386
dc.identifier.urlhttps://www.scopus.com/pages/publications/85121269670
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00667%2F2020/PT
dc.relationResearch and Development Unit for Mechanical and Industrial Engineering
dc.subjectAnalytic network process
dc.subjectAutomotive industry
dc.subjectBusiness intelligence
dc.subjectContinuous improvement
dc.subjectKey performance indicators
dc.subjectGeography, Planning and Development
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectEnvironmental Science (miscellaneous)
dc.subjectEnergy Engineering and Power Technology
dc.subjectManagement, Monitoring, Policy and Law
dc.subjectSDG 7 - Affordable and Clean Energy
dc.titleKey performance indicators selection through an analytic network process model for tooling and die industryen
dc.typejournal article
degois.publication.issue24
degois.publication.titleSustainability
degois.publication.volume13
dspace.entity.typePublication
oaire.awardNumberUIDB/00667/2020
oaire.awardTitleResearch and Development Unit for Mechanical and Industrial Engineering
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00667%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication1a4033c7-5add-4c00-8393-eb4bed8a7a8e
relation.isProjectOfPublication.latestForDiscovery1a4033c7-5add-4c00-8393-eb4bed8a7a8e

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