Business analytics in industry 4.0: a systematic review
Autor(a) principal: | |
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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/1822/73739 |
Resumo: | Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities. |
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Business analytics in industry 4.0: a systematic reviewArtificial IntelligenceIndustry 4.0Machine learningOptimizationPredictive and Prescriptive AnalyticsIndustry 40Ciências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyIndústria, inovação e infraestruturasRecently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions.WileyUniversidade do MinhoSilva, António JoãoCortez, PauloPereira, CarlosPilastri, André20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/73739engSilva, A. J., Cortez, P., Pereira, C., & Pilastri, A. (2021). Business analytics in Industry 4.0: A systematic review. Expert Systems, e12741. https://doi.org/10.1111/exsy.127410266-472010.1111/exsy.12741The original publication is available at: https://onlinelibrary.wiley.com/doi/10.1111/exsy.12741info: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:RCAAP2023-07-21T11:58:50Zoai:repositorium.sdum.uminho.pt:1822/73739Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:48:36.846768Repositório 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 |
Business analytics in industry 4.0: a systematic review |
title |
Business analytics in industry 4.0: a systematic review |
spellingShingle |
Business analytics in industry 4.0: a systematic review Silva, António João Artificial Intelligence Industry 4.0 Machine learning Optimization Predictive and Prescriptive Analytics Industry 4 0 Ciências Naturais::Ciências da Computação e da Informação Science & Technology Indústria, inovação e infraestruturas |
title_short |
Business analytics in industry 4.0: a systematic review |
title_full |
Business analytics in industry 4.0: a systematic review |
title_fullStr |
Business analytics in industry 4.0: a systematic review |
title_full_unstemmed |
Business analytics in industry 4.0: a systematic review |
title_sort |
Business analytics in industry 4.0: a systematic review |
author |
Silva, António João |
author_facet |
Silva, António João Cortez, Paulo Pereira, Carlos Pilastri, André |
author_role |
author |
author2 |
Cortez, Paulo Pereira, Carlos Pilastri, André |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Silva, António João Cortez, Paulo Pereira, Carlos Pilastri, André |
dc.subject.por.fl_str_mv |
Artificial Intelligence Industry 4.0 Machine learning Optimization Predictive and Prescriptive Analytics Industry 4 0 Ciências Naturais::Ciências da Computação e da Informação Science & Technology Indústria, inovação e infraestruturas |
topic |
Artificial Intelligence Industry 4.0 Machine learning Optimization Predictive and Prescriptive Analytics Industry 4 0 Ciências Naturais::Ciências da Computação e da Informação Science & Technology Indústria, inovação e infraestruturas |
description |
Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z |
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/1822/73739 |
url |
http://hdl.handle.net/1822/73739 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Silva, A. J., Cortez, P., Pereira, C., & Pilastri, A. (2021). Business analytics in Industry 4.0: A systematic review. Expert Systems, e12741. https://doi.org/10.1111/exsy.12741 0266-4720 10.1111/exsy.12741 The original publication is available at: https://onlinelibrary.wiley.com/doi/10.1111/exsy.12741 |
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.publisher.none.fl_str_mv |
Wiley |
publisher.none.fl_str_mv |
Wiley |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 |
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