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

• Autor(a) principal: Silva, Airton M. [UNESP]
• Data de Publicação: 2022
• Outros Autores: Tramarico, Claudemir L. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1504/IJISM.2022.124420; http://hdl.handle.net/11449/240577
• topic: AHP; analytic hierarchy process; big data; big data analytics; critical success factors; supply chain management

This 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.