Multi-criteria analysis of big data and big data analytics on supply chain management

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSilva, Airton M.-
Autor(es): dc.creatorTramarico, Claudemir L.-
Data de aceite: dc.date.accessioned2025-08-21T20:28:33Z-
Data de disponibilização: dc.date.available2025-08-21T20:28:33Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1504/IJISM.2022.124420-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240577-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240577-
Descrição: dc.descriptionThis article proposes a procedure evaluating the implementation of big data and big data analytics in supply chain management through critical success factors. With the current use of big data and big data analytics technologies, structured or non-structured data have become more important in decision-making, making the process more efficient. In addition to highlighting the main critical success factors encountered in the literature, the authors developed a classification of factors using the benefits, opportunities, costs, and risks model (BOCR). In this study, the analytic hierarchy process (AHP), a multi-criteria analysis method, is applied by considering BOCR model as the main criteria in the evaluation, and big data and big data analytics as the two main alternatives. The main contributions of this work are an identification of the main critical success factors through research found in the available literature and the proposal of a procedure for evaluating the best alternative to implementing data technology in supply chain management. The proposed approach was used to evaluate the BOCR through the real implementation of data technology.-
Descrição: dc.descriptionFaculdade de Engenharia Universidade Estadual Paulista Julio de Mesquita Filho, Campus de Guaratinguetá, Av. Dr. Ariberto Pereira da Cunha, 333-Pedregulho, SP-
Descrição: dc.descriptionFaculdade de Engenharia Universidade Estadual Paulista Julio de Mesquita Filho, Campus de Guaratinguetá, Av. Dr. Ariberto Pereira da Cunha, 333-Pedregulho, SP-
Formato: dc.format280-303-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Integrated Supply Management-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAHP-
Palavras-chave: dc.subjectanalytic hierarchy process-
Palavras-chave: dc.subjectbig data-
Palavras-chave: dc.subjectbig data analytics-
Palavras-chave: dc.subjectcritical success factors-
Palavras-chave: dc.subjectsupply chain management-
Título: dc.titleMulti-criteria analysis of big data and big data analytics on supply chain management-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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