Key performance indicators selection through an analytic network process model for tooling and die industry
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , |
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. |
id |
RCAP_6f4ee4117c0930ed9a0c99a9ef83fc92 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/130386 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799138070872719360 |