Industry 5.0 and Data Science
implementation of data capture from a human-centered perspective in a metal-mechanic company
DOI:
https://doi.org/10.14488/BJOPM.2682.2026Keywords:
Data-driven organization, Data capture, Industry 5.0, Human-centered approachAbstract
Objective: To analyze the implementation of data capture in a medium-sized metal-mechanic industry, considering factors related to Digital Transformation (DT) under the human-centered approach of Industry 5.0 (I5.0).
Methodology: Quali-quantitative study developed through action research in five phases (Oct/2023 – Jul/2024), and a survey with its instrument based on the UTAUT and MD3M models.
Findings: Six forms were identified, containing 108 fields involving 51 unique attributes, with high redundancy in the entry of general descriptions. The digital data collection, integrated with the company's ERP system, concluded with 10 forms and 35 unique variables, reducing redundant fields by 72%. Employees were involved throughout the process, with the most interaction occurring during training sessions and analysis meetings. The survey results show over 87% acceptance across the four main constructs of the UTAUT model, and the correlations between variables and participation levels indicate a reduction in users’ insecurities regarding the adoption of new technology, reinforcing that the human-centered approach was essential for the acceptance of the new data collection format.
Research limitations: Technology implementation time, absence of Industry 5.0 maturity models and inability to measure data-management maturity using the MD3M model.
Practical implications: Identification of challenges in IT-operations coordination; highlighting the need for an empathetic and collaborative approach in DT, as revealed by the reduction of resistance through employee participation in the process.
Originality: Practical demonstration that the challenges of DT can be overcome through the active participation of employees, highlighting — through the correlations found — the impact of a human-centered approach on technology implementation.
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References
Alves, J., Lima, T.M., Gaspar, P.D., 2023. Is Industry 5.0 a Human-Centred Approach? A Systematic Review. Processes 11, 193. https://doi.org/10.3390/pr11010193
Barbieri, C., 2020. Governança de dados: Práticas, conceitos e novos caminhos, 1o Ed. ed. Alta Books, Rio de Janeiro - RJ.
Cillo, V., Gregori, G.L., Daniele, L.M., Caputo, F., Bitbol-Saba, N., 2022. Rethinking companies’ culture through knowledge management lens during Industry 5.0 transition. JKM 26, 2485–2498. https://doi.org/10.1108/JKM-09-2021-0718
CNI, C.N. da I., 2024. A Importância da Indústria para o Brasil.
Colombari, R., Geuna, A., Helper, S., Martins, R., Paolucci, E., Ricci, R., Seamans, R., 2023. The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and U.S. automotive industries. International Journal of Production Economics 255, 108718. https://doi.org/10.1016/j.ijpe.2022.108718
Cruzara, G., Takahashi, A.R.W., Sandri, E.C., Cherobim, A.P.M.S., 2020. The impact of digital transformation and industry 4.0 on the aspects of value: Evidence from a meta-synthesis. Contextus 18, 92–106. https://doi.org/10.19094/contextus.2020.43717
De Sordi, J.O., 2019. Modelagem de dados: estudos de casos abrangentes da concepção lógica à implementação. Érica, São Paulo - SP.
Demir, K.A., Döven, G., Sezen, B., 2019. Industry 5.0 and Human-Robot Co-working. Procedia Computer Science 158, 688–695. https://doi.org/10.1016/j.procs.2019.09.104
Dikhanbayeva, D., Shaikholla, S., Suleiman, Z., Turkyilmaz, A., 2020. Assessment of Industry 4.0 Maturity Models by Design Principles. Sustainability 12, 9927. https://doi.org/10.3390/su12239927
European Commission, 2021a. Industry 5.0, a transformative vision for Europe (ESIR Policy Brief No. 3). Directorate-General for Research and Innovation, Bruxelas.
European Commission, 2021b. Industry 5.0: towards a sustainable, human centric and resilient European industry. Bruxelas.
Fischer, H., Wiener, M., Strahringer, S., Kotlarsky, J., Bley, K., 2023. Data-Driven Organizations: Review, Conceptual Framework, and Empirical Illustration. AJIS 27. https://doi.org/10.3127/ajis.v27i0.4425
GNU, 2023. GNU PSPP [WWW Document]. GNU Operating System. URL https://www.gnu.org/software/pspp/ (accessed 10.28.23).
Han, H., Trimi, S., 2022. Towards a data science platform for improving SME collaboration through Industry 4.0 technologies. Technological Forecasting and Social Change 174, 121242. https://doi.org/10.1016/j.techfore.2021.121242
Hasegawa, H.L., Lima, R.S.D., Mota Junior, V.D.D., Teixeira, R.L.P., 2025. Challenges in data collection for enhancing productivity in Brazilian industrial processes. BJO&PM 22, 2445. https://doi.org/10.14488/BJOPM.2445.2025
Hein-Pensel, F., Winkler, H., Brückner, A., Wölke, M., Jabs, I., Mayan, I.J., Kirschenbaum, A., Friedrich, J., Zinke-Wehlmann, C., 2023. Maturity assessment for Industry 5.0: A review of existing maturity models. Journal of Manufacturing Systems 66, 200–210. https://doi.org/10.1016/j.jmsy.2022.12.009
Hupperz, M., Gür, I., Möller, F., Otto, B., 2021. What is a Data-Driven Organization?, in: AMCIS 2021 PROCEEDINGS. Presented at the Twenty-Seventh Americas Conference on Information Systems, Montreal - Canadá.
Kayabay, K., Gökalp, M.O., Gökalp, E., Erhan Eren, P., Koçyiğit, A., 2022. Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization. Technological Forecasting and Social Change 174, 121264. https://doi.org/10.1016/j.techfore.2021.121264
Khamaisi, R.K., Brunzini, A., Grandi, F., Peruzzini, M., Pellicciari, M., 2022. UX assessment strategy to identify potential stressful conditions for workers. Robotics and Computer-Integrated Manufacturing 78, 102403. https://doi.org/10.1016/j.rcim.2022.102403
Khan, T., Emon, M.M.H., 2025. Supply chain performance in the age of Industry 4.0: evidence from manufacturing sector. BJO&PM 22, 2434. https://doi.org/10.14488/BJOPM.2434.2025
Kolade, O., Owoseni, A., 2022. Employment 5.0: The work of the future and the future of work. Technology in Society 71, 102086. https://doi.org/10.1016/j.techsoc.2022.102086
Liebowitz, J., Beckman, T., 1998. Knowledge Organizations: What Every Manager Should Know, 1a Ed. ed. CRC Press LLC, Boca Raton - Florida.
Lima, M., 2021. O que é a correlação de Spearman? [WWW Document]. Blog Psicometria Online. URL https://www.blog.psicometriaonline.com.br/o-que-e-correlacao-de-spearman/ (accessed 9.15.24).
Longo, F., Padovano, A., Umbrello, S., 2020. Value-Oriented and Ethical Technology Engineering in Industry 5.0: A Human-Centric Perspective for the Design of the Factory of the Future. Applied Sciences 10, 4182. https://doi.org/10.3390/app10124182
Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., Wang, L., Qin, Z., Bao, J., 2022. Outlook on human-centric manufacturing towards Industry 5.0. Journal of Manufacturing Systems 62, 612–627. https://doi.org/10.1016/j.jmsy.2022.02.001
Madsen, D.Ø., Berg, T., 2021. An Exploratory Bibliometric Analysis of the Birth and Emergence of Industry 5.0. ASI 4, 87. https://doi.org/10.3390/asi4040087
Margherita, E.G., Braccini, A.M., 2021. Managing industry 4.0 automation for fair ethical business development: A single case study. Technological Forecasting and Social Change 172, 121048. https://doi.org/10.1016/j.techfore.2021.121048
Moraes, S.D.S., 2023. Comunicação para inovação: contribuições da Gestão da Informação, Gestão do Conhecimento e Competência em Informação (Tese). Universidade Estadual Paulista “Júlio de Mesquita Filho,” Marília - SP.
Nahavandi, S., 2019. Industry 5.0—A Human-Centric Solution. Sustainability 11, 4371. https://doi.org/10.3390/su11164371
Nonaka, I., Takeuchi, Hirotaka, 2008. Gestão do Conhecimento. Bookman, Porto Alegre - RS.
Özdemir, V., Hekim, N., 2018. Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, “The Internet of Things” and Next-Generation Technology Policy. OMICS: A Journal of Integrative Biology 22, 65–76. https://doi.org/10.1089/omi.2017.0194
Pereira, M.A., Neumann, F.B., Milani, A.M.P., Brandão, D. dos S., Neto, R.M., 2019. Framework de Big Data, 1o Ed. ed. Sagah, Porto Alegre - RS.
Pereira, R., Santos, N. dos, 2022. Indústria 5.0: reflexões sobre uma nova abordagem paradigmática para a indústria, in: Indústria 5.0: Reflexões Sobre Uma Nova Abordagem Paradigmática Para a Indústria. Presented at the XLVI Encontro da ANPAD, ANPAD, Maringá-MG.
Pietzka, K., 2012. MD3M Master Data Management Maturity Model - Developing an Assessment to Evaluate on Organization’s MDM Maturity. University of Utrecht, Utrecht - Netherlands.
Prodanov, C.C., Freitas, E.C. de, 2013. Metodologia do trabalho científico: métodos e técnicas da pesquisa e do trabalho acadêmico, 2o ed. ed. Universidade Feevale, Novo Hamburgo - RS.
Rautenberg, S., Carmo, P.R.V.D., 2019. Big data e ciência de dados: complementariedade conceitual no processo de tomada de decisão. BRAJIS 13, 56–67. https://doi.org/10.36311/1981-1640.2019.v13n1.06.p56
Ritter, T., Pedersen, C.L., 2020. Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future. Industrial Marketing Management 86, 180–190. https://doi.org/10.1016/j.indmarman.2019.11.019
Rogers, D.L., 2017. Transformação Digital: Repensando o Seu Negócio para a Era Digital, 1a ed. ed. Autêntica, São Paulo - SP.
Saniuk, S., Grabowska, S., Straka, M., 2022. Identification of Social and Economic Expectations: Contextual Reasons for the Transformation Process of Industry 4.0 into the Industry 5.0 Concept. Sustainability 14, 1391. https://doi.org/10.3390/su14031391
Tiensuu, H., Tamminen, S., Puukko, E., Röning, J., 2021. Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing. Applied Sciences 11, 10897. https://doi.org/10.3390/app112210897
UNIDO, U.N.I.D.O., 2024. International Yearbook of Industrial Statistics 2024, Edition 2024. ed. United Nations, Erscheinungsort nicht ermittelbar.
UNIDO, U.N.I.D.O., 2022. International Yearbook of Industrial Statistics, Edition 2022. ed.
Venkatesh, Morris, Davis, Davis, 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 425. https://doi.org/10.2307/30036540
Vial, G., 2019. Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems 28, 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Visvizi, A., Troisi, O., Grimaldi, M., Loia, F., 2022. Think human, act digital: activating data-driven orientation in innovative start-ups. EJIM 25, 452–478. https://doi.org/10.1108/EJIM-04-2021-0206
Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L., 2021. Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems 61, 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006
Zizic, M.C., Mladineo, M., Gjeldum, N., Celent, L., 2022. From Industry 4.0 towards Industry 5.0: A Review and Analysis of Paradigm Shift for the People, Organization and Technology. Energies 15, 5221. https://doi.org/10.3390/en15145221
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Copyright (c) 2026 Israela Peixer Lorenzini, Cristina Keiko Yamaguchi, Merisandra Côrtes de Mattos

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