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

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Diogo
Data de Publicação: 2021
Outros Autores: Godina, Radu, da Cruz, Pedro Espadinha
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/130386
Resumo: In 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.
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spelling Key performance indicators selection through an analytic network process model for tooling and die industryAnalytic network processAutomotive industryBusiness intelligenceContinuous improvementKey performance indicatorsGeography, Planning and DevelopmentRenewable Energy, Sustainability and the EnvironmentEnvironmental Science (miscellaneous)Energy Engineering and Power TechnologyManagement, Monitoring, Policy and LawSDG 7 - Affordable and Clean EnergyIn 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.DEMI - Departamento de Engenharia Mecânica e IndustrialUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e IndustrialRUNRodrigues, DiogoGodina, Raduda Cruz, Pedro Espadinha2022-01-06T23:52:17Z2021-12-142021-12-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/130386eng2071-1050PURE: 35593030https://doi.org/10.3390/su132413777info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:09:03Zoai:run.unl.pt:10362/130386Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:45.625723Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Key performance indicators selection through an analytic network process model for tooling and die industry
title Key performance indicators selection through an analytic network process model for tooling and die industry
spellingShingle Key performance indicators selection through an analytic network process model for tooling and die industry
Rodrigues, Diogo
Analytic network process
Automotive industry
Business intelligence
Continuous improvement
Key performance indicators
Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Environmental Science (miscellaneous)
Energy Engineering and Power Technology
Management, Monitoring, Policy and Law
SDG 7 - Affordable and Clean Energy
title_short Key performance indicators selection through an analytic network process model for tooling and die industry
title_full Key performance indicators selection through an analytic network process model for tooling and die industry
title_fullStr Key performance indicators selection through an analytic network process model for tooling and die industry
title_full_unstemmed Key performance indicators selection through an analytic network process model for tooling and die industry
title_sort Key performance indicators selection through an analytic network process model for tooling and die industry
author Rodrigues, Diogo
author_facet Rodrigues, Diogo
Godina, Radu
da Cruz, Pedro Espadinha
author_role author
author2 Godina, Radu
da Cruz, Pedro Espadinha
author2_role author
author
dc.contributor.none.fl_str_mv DEMI - Departamento de Engenharia Mecânica e Industrial
UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
RUN
dc.contributor.author.fl_str_mv Rodrigues, Diogo
Godina, Radu
da Cruz, Pedro Espadinha
dc.subject.por.fl_str_mv Analytic network process
Automotive industry
Business intelligence
Continuous improvement
Key performance indicators
Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Environmental Science (miscellaneous)
Energy Engineering and Power Technology
Management, Monitoring, Policy and Law
SDG 7 - Affordable and Clean Energy
topic Analytic network process
Automotive industry
Business intelligence
Continuous improvement
Key performance indicators
Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Environmental Science (miscellaneous)
Energy Engineering and Power Technology
Management, Monitoring, Policy and Law
SDG 7 - Affordable and Clean Energy
description In 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.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-14
2021-12-14T00:00:00Z
2022-01-06T23:52:17Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/130386
url http://hdl.handle.net/10362/130386
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2071-1050
PURE: 35593030
https://doi.org/10.3390/su132413777
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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