The application of real-time overall equipment efficiency indicator in a medium-sized company

Authors

DOI:

https://doi.org/10.14488/BJOPM.2042.2024

Keywords:

OEE, IoT, Industry 4.0, Efficiency, Big Data

Abstract

Goal: This research investigated the application of real-time Overall Equipment Efficiency (OEE) at three assembly work centers in a medium-sized company. The objective was to demonstrate the feasibility of integrating Industry 4.0 technologies, such as the Internet of Things, Big Data, and Cloud Computing, in manual work center environments. It aimed to underscore the potential improvements achievable through data-driven actions facilitated by Industry 4.0 technologies, while emphasizing the significance of acquiring real-time OEE data.

Design / Methodology / Approach: The research involved theoretical exploration, implementation, data collection (Nov 2022–May 2023), and analysis on assembly workstations in a medium-sized Brazilian eyewear manufacturer.

Results: Based on the captured data, the factory implemented a series of corrective actions, leading to a reduction in unplanned stops. The obtained results were significant, as the average efficiency of the studied work centers improved by 12.3% in 7 months, with an increase in performance and in availability.

Limitations of the investigation: The analysis faces challenges due time constraints, potentially limiting the full assessment of IoT impact. Seasonal variations in eyeglass production and style-specific demand complicate evaluating the true benefits of Industry 4.0 tools, making effective OEE improvement hard to determine.

Practical implications: The study demonstrates a method to gauge manual labor efficiency through Industry 4.0 technologies.

Originality / Value: This study shows how Industry 4.0 technologies (IoT, Big Data, Cloud Computing) can be integrated into manual workforces, enhancing efficiency and providing real-time OEE for workers to self-assess.

Downloads

Download data is not yet available.

References

Albertin, M. R. and Pontes, H. L. J. (2021), “A Engenharia de Produção na Era da indústria 4.0: Estudos de casos e benchmarking da indústria 4.0”. Appris, Curitiba, Brazil.

Ammar, M. et al. (2021), “Improving material quality management and manufacturing organizations system through Industry 4.0 technologies”, Materials Today: Proceedings, Hoshiarpur. Vol. 45, No. 1, pp. 5089 – 5096. DOI: doi.org/10.1016/j.matpr.2021.01.585.

Bakhsh, A.A. and Raj, S.A. (2019), “Increasing OEE of an assembly line using the Industrial Internet of Things”, Journal of mechanics of continua and mathematical sciences. Vol. Special Issue, No. 3, pp. 155-168. DOI: doi.org/10.26782/jmcms.spl.3/2019.09.00012

Bruno, F.S. (2016), “A Quarta Revolução Industrial do Setor Têxtil e de Confecção: A Visão de Futuro para 2030”. 1st ed; Estação das Letras e Cores, São Paulo, Brazil.

Cañizares, E. and Valero, F. A. (2018), “Analyzing the effects of applying IoT to a metal-mechanical company”, Journal of Industrial Engineering and Management, Vol. 11, No. 2, pp. 308-317. Doi: dx.doi.org/10.3926/jiem.2526.

Colombo, J.F. and Filho, J.L. (2018), “INTERNET DAS COISAS (IoT) E INDÚSTRIA 4.0: revolucionando o mundo dos negócios”, Revista Interface Tecnológica, Vol. 15, No. 2, pp. 72-85. Doi: http//:10.31510/infa.v15i2.496

Correani, A. and et al. (2020), “Implementing a Digital Strategy: Learning from the Experience of Three Digital Transformation Projects”, California Management Review, Vol. 62, No. 4, pp. 37– 56. Doi: https://doi.org/10.1177/0008125620934864

Costa, M.P.S. (2023), “Análise da Computação em Nuvem aplicada à Indústria 4.0”, Final Paper in Computer engineering, Federal University of Ouro Preto, Brazil.

Ferreira, G.F.N. (2019), “Plataforma de análise e visualização de dados para sistemas IoT industriais baseada em métodos de Big Data”, Dissertation. Polytechnic Institute of Bragança, Portugal.

Fonseca, J.J.S. (2002), “Metodologia da pesquisa científica”. UEC, Brazil.

Freitas AA (2017), “A internet das coisas e seus efeitos na indústria 4.0”, Final Paper in Computing Systems, Federal Fluminense University, Brazil.

Herrero, A. C. and et al. (2020). “An interference-resilient IIoT solution for measuring the effectiveness of industrial processes”. IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapura, 18-21 October 2020, pp. 2155-2160, Doi: 10.1109/IECON43393.2020.9254454.

Hwang, G. et al. (2017), “Developing performance measurement system for Internet of Things and smart factory environment”, International Journal of Production Research, Milton Park, Vol. 55, No. 9, pp. 1-13. Doi: doi.org/10.1080/00207543.2016.1245883.

Klimecka-Tatar, D. and Ingaldi, M. (2022), “Digitization of processes in manufacturing SMEs-value stream mapping and OEE analysis”, Procedia Computer Science, Vol. 200, pp. 660-668. Doi: https://doi.org/10.1016/j.procs.2022.01.264

Libert, B. et al. (2016), “7 Questions to Ask before Your Next Digital Transformation,” Harvard Business Review Digital Articles, available from: https://hbr.org/2016/07/7-questions-to-ask-before-your-next-digital-transformation#. (access 01 Nov. 2023).

Leonel, V. and Motta, A.M. (2022), “Ciência e pesquisa: livro didático”, UnisulVirtual, available from: https://repositorio.animaeducacao.com.br/bitstream/ANIMA/21774/1/fulltext.pdf. (access 28 Oct. 2023).

Lindegren, M.L. et al. (2022), “Combining simulation and data analytics for oee improvement”, International Journal of Simulation Modelling (IJSIMM), Vol. 21, No. 1, pp. 29-40. Doi: https://doi.org/10.2507/IJSIMM21-1-584

Li, Y.H. et al. (2022), “Real-time OEE visualisation for downtime detection”, IEEE 20th International Conference on Industrial Informatics (INDIN), Perth, Australia, 25-28 July 2022. Doi: https:// 10.1109/INDIN51773.2022.9976067

Mussi, R.F.F. et al. (2019), “Pesquisa Quantitativa e/ou Qualitativa: distanciamentos, aproximações e possibilidades”, Revista Sustinere, Vol. 7, No. 2, pp. 414-430. Doi: https://doi.org/10.12957/sustinere.2019.41193.

Nakagima, S. (1988), “Introduction to TPM: Total Productive Maintenance”. Productivity Press, Minneapolis, United States

Neto, L.G.C. and Campos, F.C. (2021), “Oportunidades de aplicações de business intelligence no contexto da indústria 4.0: Revisão sistemática da literatura 2015-2020”, Exacta, Vol. 21, No. 2, pp. 503-519. Doi: https://doi.org/10.5585/exactaep.2021.19525.

Paula, L. and Dian, M.O. (2021), “COMPUTAÇÃO EM NUVEM: os desafios das empresas ao migrar para a nuvem”, Revista Interface Tecnológica, Vol. 18, No. 2, pp. 304-315. Doi: https:// 10.31510/infa.v18i2.1304

Paz, A.C.M. and Loos, M.J. (2020), “A importância da computação em nuvem para a indústria 4.0”, Revista Gestão Industrial, Vol. 16, No. 2, pp. 166-185. Doi: https://10.3895/gi.v16n2.9317

Pita, F.T.H. (2023), “Abordagem Big Data a dados de mobilidade em transportes públicos”, Dissertation in Master in Computer Engineering, State University of Rio de Janeiro, Brazil.

Santos B. and Lima T.M. (2018), “Indústria 4.0: desafios e oportunidades”, Revista Produção e Desenvolvimento, Vol. 4, No. 1, pp. 111-124. DOI: doi.org/10.32358/rpd.2018.v4.316

Sant’Ana, G.D. and Silva, H.R. (2020), “Análise do setor de manutenção em uma indústria de beneficiamento de semente de milho”, Brazilian Applied Science Review, Vol. 4, No. 6, pp. 3864-3887. Doi: https://doi.org/10.34115/basrv4n6-044.

Sayuti, M. et al. (2019), “Analysis of the overall equipment effectiveness (OEE) to minimize six big losses of pulp machine: a case study in pulp and paper industries”. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, Aceh, Indonesia, 21-22 Oct. 2018.

Silva, N. J. et al. (2020). “Big Data Analytics E Sua Aplicação No Indicador De Oee”, Revista Pesquisa e Ação, Vol. 6, No. 1, pp. 66-80.

Silveira, D.T. and Córdova, F.P. (2009), “A pesquisa científica. Métodos de pesquisa”. Editora da UFRGS, Porto Alegre, Brazil.

Teimoury, E. et al. (2013), “Automation of the supply chain performance measurement based on multi-agent system”, International Journal of Agile Systems and Management, Vol. 6, No. 1, pp. 25-42. Doi: https://10.1504/IJASM.2013.052225

Downloads

Published

2024-05-25

How to Cite

Novochadlo, Y. M., & Paladini, E. P. (2024). The application of real-time overall equipment efficiency indicator in a medium-sized company. Brazilian Journal of Operations & Production Management, 21(2), 2042 . https://doi.org/10.14488/BJOPM.2042.2024

Issue

Section

Case study

Most read articles by the same author(s)