Application of business intelligence in logistics

a bibliometric review

Authors

  • Francisco Lucas Nascimento Universidade Federal do Ceará (UFC), Russas, CE, Brasil.
  • Sara Monaliza Sousa Nogueira Universidade Federal do Ceará (UFC), Russas, CE, Brasil.
  • Lucelindo Dias Ferreira Junior Universidade Federal do Ceará (UFC), Russas, CE, Brasil. https://orcid.org/0000-0003-1419-4152
  • Sandro Alberto Vianna Lordelo Universidade Federal Fluminense (UFF), Niterói, RJ, Brasil. https://orcid.org/0000-0002-3046-395X

DOI:

https://doi.org/10.14488/1676-1901.v24i1.5113

Keywords:

Business intelligence, Logistics, Supply chain, Bibliometric review, Big Data

Abstract

The application of Business Intelligence (BI) in logistics has proven to be of great importance, allowing companies to make strategic decisions based on accurate data and advanced analytics. Therefore, it is crucial for companies to use data analysis tools to develop more efficient logistics management strategies. In this regard, this study aims to analyze articles that address this application through bibliometric analysis. The methodology used involved searching for articles in two databases, Web of Science and Scopus, resulting in 179 articles published between the period of 1997 and 2023. Terms related to BI in logistics were used for the research, and for the analysis of the results, the Bibliometrix package of the RStudio software was used, with the aid of the Biblioshiny visualization tool. Based on the results, an increase in scientific production in this topic was evident from 2015 onwards, with European and Asian countries contributing more to this growth, representing 11 out of the top 15 producers. In this context, the most cited article belongs to the United Kingdom and addresses the practical implications of Big Data in supply chain management. Therefore, Big Data analytics is among the most emerging topics, as it is a methodology that uses various tools to analyze and extract valuable information from large volumes of data, enabling the optimization of supply chain management. Other frequent topics in the study were "Firm Performance," "Impact," and "Data Quality," revealing research trends. This analysis contributes to a deep understanding of the evolution and trends in the application of BI in logistics, providing valuable insights for researchers, professionals, and decision-makers.

Downloads

Download data is not yet available.

Author Biographies

Francisco Lucas Nascimento, Universidade Federal do Ceará (UFC), Russas, CE, Brasil.

Engenheiro de Produção pela Universidade Federal do Ceará (UFC). Profissional na área de Engenharia de operações e processos da produção e Engenharia de métodos e tempos, atualmente atua na melhoria de processos no setor de serviços elétricos.

Sara Monaliza Sousa Nogueira, Universidade Federal do Ceará (UFC), Russas, CE, Brasil.

Dra. em Engenharia de Pesca, pela Universidade Federal do Ceará (UFC) e Dra. em Ciências (Planejamento Energético e Ambiental) pela Universidade Federal do Rio de Janeiro (PPE/COPPE/UFRJ). E Especialista em Gerenciamento de Projetos (MBA), pela Universidade Federal Fluminense (UFF).

Lucelindo Dias Ferreira Junior, Universidade Federal do Ceará (UFC), Russas, CE, Brasil.

Professor do Magistério Superior no curso de Engenharia de Produção da Universidade Federal do Ceará (UFC), Campus Russas. Doutor e Mestre em Engenharia de Produção pela Escola de Engenharia de São Carlos (EESC), Universidade de São Paulo (USP). Graduado em Engenharia de Produção Mecânica pela Universidade Federal do Ceará (UFC).

Sandro Alberto Vianna Lordelo, Universidade Federal Fluminense (UFF), Niterói, RJ, Brasil.

Professor do Magistério Superior no curso Engenharia de Produção da Universidade Federal Fluminense (UFF). Doutor, mestre e graduado em Engenharia de Produção pela Universidade Federal Fluminense (UFF).

References

APPELBAUM, D.; KOGAN, A.; VASARHELYI, M.; YAN, Z. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, v. 25, p. 29-44, 2017.

ARIA, M.; CUCURULLO, C. Bibliometrix: An R-Tool for comprehensive science mapping analysis. Journal of informetrics, v. 11, n. 4, p. 959-975, 2017.

ARUNACHALAM, D.; KUMAR, N.; KAWALEK, J. P. Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, v. 114, p. 416-436, 2018.

BALLOU, Ronald H. Gerenciamento da Cadeia de Suprimentos/Logística Empresarial. 5 ed. São Paulo: Bookman, 2010.

BALLOU, Ronald H. Logística Empresarial: transporte, administração de materiais e distribuição física. Atlas, 1 ed. 2013.

CHOPRA, S.; MEINDL, P. Supply Chain Management: Strategy, Planning, and Operation. Pearson Prentice Hall. 2007.

CHRISTOPHER, Martin. Logística e Gerenciamento da Cadeia de Suprimentos. 5 ed. São Paulo: Cengage Learning, 2019.

DUBEY, R.; GUNASEKARAN, A.; CHILDE, S. J. Big data analytics capability in supply chain agility. Management Decision, 2018.

GHASEMAGHAEI, M.; CALIC, G. Assessing the impact of Big Data on firm innovation performance: Big Data is not always better data. Journal of Business Research, p. 147-162, 2020.

GHASEMAGHAEI, M.; HASSANEIN, K.; TUREL, O. Increasing firm agility through the use of data analytics: The role of fit. Decision Support Systems, v. 101, p. 95–105, 2017.

GRABIŃSKA, A.; ZIORA, L. The Application of Business Intelligence Systems in Logistics: Review of selected practical examples. CzOTO, v. 1, n. 1, p. 1028-1035, 2019.

GRANT, David B. Gestão de Logística e Cadeia de Suprimentos. Tradução: Arlete Simille. 1 ed. São Paulo: Saraiva, 2013.

HOFMANN, E. Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, v. 55, n. 17, p. 5108-5126, 2015.

INMON, W. H. Building the Data Warehouse. 4 ed. New York: Wiley, 2005.

JAFARI, T.; ZAREI, A.; AZAR, A.; MOGHADDAM, A. The impact of business intelligence on supply chain performance with emphasis on integration and agility–a mixed research approach. International Journal of Productivity and Performance Management, v. 72, n. 5, p. 1445-1478, 2023.

JORDAN, J.; ELLEN, C. Business need, data and business intelligence. Journal of Digital Asset Management, v. 5, n. 1, p. 10-20, 2009.

KEMCZINSKI, A.; CIDRAL, A.; CASTRO, J. E. E.; FIOD NETO, M. Como obter vantagem competitiva utilizando Business Intelligence? Revista Produção Online, v. 3, n. 2, 2003.

KOH, S. C. L.; GUNASEKARAN, A.; GOODMAN, T. Drivers, barriers and critical success factors for ERPII implementation in supply chains: A critical analysis. The Journal of Strategic Information Systems, v. 20, n. 4, p. 385-402, 2011.

MING-LANG TSENG; THI PHUONG THUY TRAN; HIEN MINH HA; TAT-DAT BUI; MING K. LIM. Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis. Journal of Industrial and Production Engineering, v. 38, p. 581-598, 2021.

NOVAES, Antônio G. Logística e gerenciamento da cadeia de distribuição: estratégia, operação e avaliação. Rio de Janeiro: GEN Atlas, 2021. 424 p.

ROSSMANN, B.; CANZANIELLO, A.; VON DER GRACHT, H. A.; HARTMANN, E. The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study. Technological Forecasting & Social Change, p. 135-149, 2018.

SAHAY, B. S.; RANJAN, J. Real time business intelligence in supply chain analytics. Information Management & Computer Security, v. 16, n. 1, p. 28-48, 2008

SEVERINO, Antônio Joaquim. Metodologia do trabalho científico. 23 ed. rev. e atual. São Paulo: Cortez, 2014.

SHASHI; CENTOBELLI, P.; CERCHIONE, R.; ERTZ, M. Agile supply chain management: where did it come from and where will it go in the era of digital transformation? Industrial Marketing Management, v. 90, p 324-345, 2020.

SINGH, A.; SHUKLA, N.; MISHRA, N. Social media data analytics to improve supply chain management in food industries. Transportation Research Part E-Logistics and Transportation Review, v. 114, p 398-415, 2018.

TOLEDO, R. F.DE; MIRANDA JUNIOR, H.L.; FARIAS FILHO, J.R.; COSTA, H. G. A scientometric review of global research on sustainability and project management dataset. Data in brief, v. 25, 104312, 2019.

TREINTA, F. T.; FARIAS FILHO, J. R.; SANT'ANNA, A. P.; RABELO, L. M. Metodologia de pesquisa bibliográfica com a utilização de método multicritério de apoio à decisão. Production, v. 24, n. 3) p. 508-520, 2014.

TURBAN, E.; KING, D.; ARONSON, J. E.; SHARDA, R. Business Intelligence: um enfoque gerencial para a inteligência do negócio. Bookman, São Paulo, 2009. ISBN: 9788577803347

TURBAN, E.; SHARDA, R.; ARONSON, J. E.; KING, D. Business intelligence: A managerial approach. 2 ed. Prentice Hall, 2011.

TURRIONI, João Batista; MELLO, Carlos Henrique Pereira. Metodologia de pesquisa em engenharia de produção - estratégias, métodos e técnicas para condução de pesquisas quantitativas e qualitativas. Minas Gerais. Cap. 1, p. 8-11, 2012.

Published

2024-04-06

How to Cite

Nascimento, F. L., Nogueira, S. M. S., Ferreira Junior, L. D. ., & Lordelo, S. A. V. (2024). Application of business intelligence in logistics: a bibliometric review. Revista Produção Online, 24(1), 5113 . https://doi.org/10.14488/1676-1901.v24i1.5113