Oportunidades de aplicações de Business Intelligence no contexto da indústria 4.0: revisão sistemática da literatura 2015-2020
DOI:
https://doi.org/10.5585/exactaep.2021.19525Palavras-chave:
Indústria 4.0, Business Intelligence, dashboard, IoT, qualificação profissionalResumo
A integração de sistemas de informação favorece a tomada de decisão que pode ter suporte das tecnologias de Business Intelligence, via relatórios e dashboards. Isso facilita a coleta de dados, análises e entrega de informações, para compor as bases decisórias da gestão de operações. No contexto da Indústria 4.0 o Business Intelligence conecta-se via internet of things. O objetivo desse artigo é identificar as oportunidades de aplicações de Business Intelligence na Indústria 4.0. Para tanto, foi realizada uma Revisão Sistemática da Literatura via protocolo nas bases de dados: Scopus, Web of Science e Science Direct. Ao final, dezenove artigos selecionados, pelo critério do maior número de citações, identificaram onze tópicos principais. O impacto social vem pela demanda de profissionais melhor qualificados para Indústria 4.0. Em termos práticos a internet of things será usada massivamente para apoiar processos decisórios. Academicamente, esta síntese da literatura contribui para a melhor compreensão da temática.
Downloads
- Citations
- Citation Indexes: 1
- Captures
- Readers: 9
Referências
Ahmad, S., Miskon, S., Alabdan, R., & Tlili, I. (2020). Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0. Sustainability, 12(7), 2632. https://doi.org/10.3390/su12072632
Ansari, F., Glawar, R., & Nemeth, T. (2019). PriMa: a prescriptive maintenance model for cyber-physical production systems. International Journal of Computer Integrated Manufacturing, 32(4-5), 482-503. https://doi.org/10.1080/0951192X.2019.1571236
Aqel, M. J., Nakshabandi, O. A., & Adeniyi, A. (2019). Decision support systems classification in industry. Periodicals of Engineering and Natural Sciences, 7(2), 774-785. DOI: http://dx.doi.org/10.21533/pen.v7i2.550
Benešová, A., & Tupa, J. (2017). Requirements for education and qualification of people in Industry 4.0. Procedia Manufacturing, 11, 2195-2202. https://doi.org/10.1016/j.promfg.2017.07.366
Barbieri C. (2001). BI – Business Intelligence: modelagem & tecnologia. Rio de Janeiro: Axcel Books, 424p.
Cao, L., Cai, Y., & Yue, Y. (2019). Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions. IEEE Access, 7, 161524-161553. DOI: http://doi.org/10.1109/ACCESS.2019.2951370
Coito, T., Viegas, J. L., Martins, M. S., Cunha, M. M., Figueiredo, J., Vieira, S. M., & Sousa, J. M. (2019). A Novel Framework for Intelligent Automation. IFAC-PapersOnLine, 52(13), 1825-1830. https://doi.org/10.1016/j.ifacol.2019.11.501
Eggert, M., & Alberts, J. (2020). Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda. Business Research, 1-55. https://doi.org/10.1007/s40685-020-00108-y
García, S. G., & García, M. G. (2019). Industry 4.0 implications in production and maintenance management: An overview. Procedia Manufacturing, 41, 415-422. https://doi.org/10.1016/j.promfg.2019.09.027
Goti, A., De la Calle, A., Gil, M. J., Errasti, A., Bom, P. R., & García-Bringas, P. (2018). Development and application of an assessment complement for production system audits based on data quality, IT infrastructure, and sustainability. Sustainability, 10(12), 4679. https://doi.org/10.3390/su10124679
Goti, A., Oyarbide-Zubillaga, A., Alberdi, E., Sanchez, A., & Garcia-Bringas, P. (2019). Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms. Applied Sciences, 9(15), 3068. https://doi.org/10.3390/app9153068
Hänel, T., & Felden, C. (2017). Design and evaluation of an analytical framework to analyze and control production processes. Procedia CIRP, 62, 141-146. https://doi.org/10.1016/j.procir.2016.06.052
Jerman, A., Erenda, I., & Bertoncelj, A. (2019). The influence of critical factors on business model at a smart factory: A case study. Business Systems Research Journal, 10(1), 42-52. DOI: http://doi.org/10.2478/bsrj-2019-0004
Mantravadi, S., & Møller, C. (2019). An overview of next-generation manufacturing execution systems: how important is MES for industry 4.0?. Procedia manufacturing, 30, 588-595. https://doi.org/10.1016/j.promfg.2019.02.083
Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. (2018). The role and impact of Industry 4.0 and the internet of things on the business strategy of the value chain—the case of Hungary. Sustainability, 10(10), 3491. https://doi.org/10.3390/su10103491
Odważny, F., Wojtkowiak, D., Cyplik, P., & Adamczak, M. (2019). Concept for measuring organizational maturity supporting sustainable development goals. LogForum, 15. http://doi.org/10.17270/J.LOG.2019.321
Olszak, C. M., & Mach-Król, M. (2018). A conceptual framework for assessing an organization’s readiness to adopt big data. Sustainability, 10(10), 3734. https://doi.org/10.3390/su10103734
Pereira, A. C., & Romero, F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 13, 1206-1214. https://doi.org/10.1016/j.promfg.2017.09.032
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58. https://doi.org/10.1016/j.accinf.2018.03.001
Ruppert, T., Jaskó, S., Holczinger, T., & Abonyi, J. (2018). Enabling technologies for operator 4.0: a survey. Applied sciences, 8(9), 1650. https://doi.org/10.3390/app8091650
Saqlain, M., Piao, M., Shim, Y., & Lee, J. Y. (2019). Framework of an IoT-based Industrial Data Management for Smart Manufacturing. Journal of Sensor and Actuator Networks, 8(2), 25. https://doi.org/10.3390/jsan8020025
Sayfouri, N. An alternative method of literature review: systematic review in english language teaching research. Procedia - Social and Behavioral Sciences, v.98, n.1, p.1693–1697, 2014.
Soni, N., Sharma, E. K., Singh, N., & Kapoor, A. (2020). Artificial Intelligence in Business: From Research and Innovation to Market Deployment. Procedia Computer Science, 167, 2200-2210. https://doi.org/10.1016/j.procs.2020.03.272
Schwab, K. (2016). A Quarta Revolução Industrial; Tradução Daniel Moreira Miranda, São Paulo, Edipro, ISBN 978-85-7283-978-5. http://creativecommons.org/licenses/by-nc-nd/4.0
Start – State of the Through Systematic Review, versão 3.4 Beta – LAPES – Laboratório de Pesquisa em Engenharia de Software - UFSCAR.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375
Reim, W., Parida, V. e Ortqvist, D., Product – Service Systems (PSS) business models and tactis – systematic literature review. Journal of Cleaner Production, v.97, n.1, p.61-75, 2015. https://doi.org/10.1016/j.jclepro.2014.07.003
StArt. Software para apoio à Revisão Sistemática da Literatura. Desenvolvido pelo Laboratório LAPES-UFSCar. Disponível para download em: http://lapes.dc.ufscar.br/tools/start_tool
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2021 Dos autores
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
10.24857/rgsa.v18n7-014