Uncertainty assessment of performance indicators
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Idioma: | eng |
Título da fonte: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Texto Completo: | http://hdl.handle.net/1822/31239 |
Resumo: | Purpose: This paper defines a model to evaluate the uncertainty in performance indicators (PIs) based on Uncertainty Components (UCs). Methodology: The proposed work consists, in a first stage, of an assessment of the level of influence that each UC has in a given PI. Based on the questionnaire responses a matrix of UCs vs PIs is presented to show the relevance of the contribution of each UC to the uncertainty associated with a PI. The second stage of the methodology consists on the development of a model to infer the uncertainty level on a PI based on the uncertainty level of the identified UCs. Findings: A questionnaire referring to the assessment of PIs was applied, and the results provide evidence that UCs influence the PI. A model was developed based on logical relations between the UCs and the overall PI uncertainty, and the number of empirical analyses contribute to validate it. Originality/value: This paper presents a model to infer the uncertainty level of a PI based on UCs. The model can also be applied to propagate uncertainty among multiple related PIs. UCs definitions can guide the development of actions to reduce uncertainty in PIs, thus reducing the risk in the decision making process. |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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https://opendoar.ac.uk/repository/7160 |
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Uncertainty assessment of performance indicatorsUncertaintyperformance indicatorsFuzzy logicEngenharia e Tecnologia::Outras Engenharias e TecnologiasPurpose: This paper defines a model to evaluate the uncertainty in performance indicators (PIs) based on Uncertainty Components (UCs). Methodology: The proposed work consists, in a first stage, of an assessment of the level of influence that each UC has in a given PI. Based on the questionnaire responses a matrix of UCs vs PIs is presented to show the relevance of the contribution of each UC to the uncertainty associated with a PI. The second stage of the methodology consists on the development of a model to infer the uncertainty level on a PI based on the uncertainty level of the identified UCs. Findings: A questionnaire referring to the assessment of PIs was applied, and the results provide evidence that UCs influence the PI. A model was developed based on logical relations between the UCs and the overall PI uncertainty, and the number of empirical analyses contribute to validate it. Originality/value: This paper presents a model to infer the uncertainty level of a PI based on UCs. The model can also be applied to propagate uncertainty among multiple related PIs. UCs definitions can guide the development of actions to reduce uncertainty in PIs, thus reducing the risk in the decision making process.Universidade do MinhoCavallare, MarcelloSousa, SérgioNunes, Eusébio P.20142014-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/31239enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T07:38:00Zoai:repositorium.sdum.uminho.pt:1822/31239Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:34:11.933897Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Uncertainty assessment of performance indicators |
title |
Uncertainty assessment of performance indicators |
spellingShingle |
Uncertainty assessment of performance indicators Cavallare, Marcello Uncertainty performance indicators Fuzzy logic Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
title_short |
Uncertainty assessment of performance indicators |
title_full |
Uncertainty assessment of performance indicators |
title_fullStr |
Uncertainty assessment of performance indicators |
title_full_unstemmed |
Uncertainty assessment of performance indicators |
title_sort |
Uncertainty assessment of performance indicators |
author |
Cavallare, Marcello |
author_facet |
Cavallare, Marcello Sousa, Sérgio Nunes, Eusébio P. |
author_role |
author |
author2 |
Sousa, Sérgio Nunes, Eusébio P. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Cavallare, Marcello Sousa, Sérgio Nunes, Eusébio P. |
dc.subject.por.fl_str_mv |
Uncertainty performance indicators Fuzzy logic Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
topic |
Uncertainty performance indicators Fuzzy logic Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
description |
Purpose: This paper defines a model to evaluate the uncertainty in performance indicators (PIs) based on Uncertainty Components (UCs). Methodology: The proposed work consists, in a first stage, of an assessment of the level of influence that each UC has in a given PI. Based on the questionnaire responses a matrix of UCs vs PIs is presented to show the relevance of the contribution of each UC to the uncertainty associated with a PI. The second stage of the methodology consists on the development of a model to infer the uncertainty level on a PI based on the uncertainty level of the identified UCs. Findings: A questionnaire referring to the assessment of PIs was applied, and the results provide evidence that UCs influence the PI. A model was developed based on logical relations between the UCs and the overall PI uncertainty, and the number of empirical analyses contribute to validate it. Originality/value: This paper presents a model to infer the uncertainty level of a PI based on UCs. The model can also be applied to propagate uncertainty among multiple related PIs. UCs definitions can guide the development of actions to reduce uncertainty in PIs, thus reducing the risk in the decision making process. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 2014-01-01T00:00:00Z |
dc.type.driver.fl_str_mv |
conference paper |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/31239 |
url |
http://hdl.handle.net/1822/31239 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia instacron:RCAAP |
instname_str |
FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
collection |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
repository.name.fl_str_mv |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
repository.mail.fl_str_mv |
info@rcaap.pt |
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1833596025996050432 |