Dashboards in SAP Business Intelligence for Decision Making in the Automotive Industry: A Systematic Review

Descripción del Articulo

Decision-making allows organizations to be more agile and efficient in such a competitive market, where data is considered the primary asset for generating valuable information. This article presents a systematic review using the PRISMA methodology to analyze the use of dashboards in SAP Business In...

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Detalles Bibliográficos
Autores: Zegarra Chamorro, Luz Giannina, Ormeño Audante, Ayrton Gustavo
Formato: artículo
Fecha de Publicación:2025
Institución:Universidad Señor de Sipan
Repositorio:Revistas - Universidad Señor de Sipán
Lenguaje:español
OAI Identifier:oai:revistas.uss.edu.pe:article/2863
Enlace del recurso:https://revistas.uss.edu.pe/index.php/EPT/article/view/2863
Nivel de acceso:acceso abierto
Materia:Dashboard
SAP BI
toma de decisiones
Industria automotriz
Business Intelligence
Dashboards
decision-making
automotive industry
Descripción
Sumario:Decision-making allows organizations to be more agile and efficient in such a competitive market, where data is considered the primary asset for generating valuable information. This article presents a systematic review using the PRISMA methodology to analyze the use of dashboards in SAP Business Intelligence (SAP BI) for decision-making in the automotive industry. An extensive search was conducted in the Scopus database, which, after applying various inclusion and exclusion criteria, resulted in a total of 21 articles for the corresponding analysis, based on the research questions posed. The main findings indicate that dashboards facilitate quick and efficient access to critical information, improving the alignment between strategic objectives and daily operations within organizations. However, challenges in their implementation were also identified, such as the need for proper training and the variability in their effectiveness depending on the organizational context. The conclusions emphasize the importance of integrating dashboards into BI systems to optimize decision-making in the automotive industry, as well as the need to address the challenges associated with managing these tools. Future research suggests expanding the scope to other sectors and examining the impact of organizational culture on the adoption of these tools
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