Gestão & Produção
Gestão & Produção
Artigo Original

Decision making in the process of choosing and deploying industry 4.0 technologies

jocieli francisco da silva; flávia luana da silva; débora oliveira da silva; luiz alberto oliveira rocha; ágata maitê ritter

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Abstract: Advances in the development and use of Industry 4.0 technology has resulted in it being applied in various industries. Industry 4.0 enables companies to develop their operations and improve their organizational efficiency. The process of implementing Industry 4.0 is very important, as it requires significant investment, skilled labor and state-of-the-art technology. The aim of this article is to identify the main criteria behind decision-making in choosing and implementing technology, that is part of the Industry 4.0 concept. The research method chosen for the study was a systematic literature review. The results show that the use of broad requirements tends to be predominant, using large groups of criteria that are intended to aggregate several different requirements. This review is relevant because few recent studies have been found, and therefore this article helps to identify what has been published so far on the subject of decision-making around choosing and implementing Industry 4.0 technology in organizations and what criteria and tools they base their decisions on. To date, the research has not encountered any similar work that aims to group all the information on decision-making around choosing and implementing Industry 4.0 technology.


Industry 4.0, 4th Industrial Revolution, Decision making, Fuzzy front end


Arcidiacono, G., & Pieroni, A. (2018). The revolution lean six sigma 4.0. International Journal on Advanced Science, Engineering and Information Technology, 8(1), 141-149.

Beyaz, H. F., & Yıldırım, N. (2020). A multi-criteria decision-making model for digital transformation in manufacturing: a case study from automotive supplier industry. In Proceedings of the International Symposium for Production Research 2019 (pp. 217-232). Cham: Springer.

Cauchick, P., Morabito, R., & Pureza, V. (2018). Metodologia de pesquisa em engenharia de produção. Rio de Janeiro: Elsevier.

Crossan, M. M., & Apaydin, M. (2010). A multi‐dimensional framework of organizational innovation: a systematic review of the literature. Journal of Management Studies, 47(6), 1154-1191.

Daneshjo, N., Majerník, M., Krivosudska, J., & Danishjoo, E. (2017). Modelling technical and economic parameters in selection of manufacturing devices. TEM Journal, 6(4), 738.

Dresch, A., Lacerda, D. P., & Antunes, J. A. V., Jr. (2015). Design science research: a method for science and technology advancement. Cham: Springer.

Erbay, H., & Yıldırım, N. (2018). Technology selection for digital transformation: a mixed decision making model of AHP and QFD. In The International Symposium for Production Research (pp. 480-493). Cham: Springer.

Erdogan, M., Ozkan, B., Karasan, A., & Kaya, I. (2018). Selecting the best strategy for Industry 4.0 applications with a case study. In F. Calisir & H. Camgoz Akdag (Eds.), Industrial engineering in the industry 4.0 era (pp. 109-119). Cham: Springer.

Faller, C., & Feldmüller, D. (2015). Industry 4.0 learning factory for regional SMEs. Procedia Cirp, 32, 88-91.

Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910-936.

Hamzeh, R., Zhong, R., Xu, X. W., Kajáti, E., & Zolotova, I. (2018, August). A technology selection framework for manufacturing companies in the context of Industry 4.0. In 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) (pp. 267-276). New York: IEEE.

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425.

Kaya, I., Erdogan, M., Karasan, A., & Ozkan, B. (2020). Creating a road map for Industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology. Soft Computing, 24(23), 17931-17956.

Keskin, F. D., Kabasakal, İ., Kaymaz, Y., & Soyuer, H. (2018). An assessment model for organizational adoption of industry 4.0 based on multi-criteria decision techniques. In The International Symposium for Production Research (pp. 85-100). Cham: Springer.

Kipper, L. M., Furstenau, L. B., Hoppe, D., Frozza, R., & Iepsen, S. (2021). Scopus scientific mapping production in Industry 4.0 (2011-2018): a bibliometric analysis. International Journal of Production Research, 15(18), 129-147.

Klingenberg, C. O., Borges, M. A. V., & Antunes, J. A. V., Jr. (2019). Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies. Journal of Manufacturing Technology Management, 32(3), 570-592.

Leone, D., & Barni, A. (2020). Industry 4.0 on demand: a value driven methodology to implement Industry 4.0. In IFIP International Conference on Advances in Production Management Systems (pp. 99-106). Cham: Springer.

Mahdiraji, H. A., Zavadskas, E. K., Skare, M., Kafshgar, F. Z. R., & Arab, A. (2020). Evaluating strategies for implementing Industry 4.0: a hybrid expert oriented approach of BWM and interval valued intuitionistic fuzzy TODIM. Economic Research-Ekonomska Istraživanja, 33(1), 1600-1620.

Morandi, M. I. W. M., & Camargo, L. F. R. (2015). Systematic literature review. In A. Dresch, D. P. Lacerda & J. A. V. Antunes Jr. (Eds.), Design science research: a method for science and technology advancement (pp. 141-175). Cham: Springer.

Muhuri, P. K., Shukla, A. K., & Abraham, A. (2019). Industry 4.0: a bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence, 78, 218-235.

Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247.

Olfati, M., Yuan, W., & Nasseri, S. H. (2020). An integrated model of fuzzy multi-criteria decision making and stochastic programming for the evaluating and ranking of advanced manufacturing technologies. Iranian Journal of Fuzzy Systems, 17(5), 183-196.

Renz, S. M., Carrington, J. M., & Badger, T. A. (2018). Two strategies for qualitative content analysis: An intramethod approach to triangulation.Qualitative health research, 28(5), 824-831.

Sanders, A., Elangeswaran, C., & Wulfsberg, J. P. (2016). Industry 4.0 implies lean manufacturing: research activities in Industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3), 811-833.

Sevinç, A., Gür, Ş., & Eren, T. (2018). Analysis of the difficulties of SMEs in Industry 4.0 applications by analytical hierarchy process and analytical network process. Processes, 6(12), 264.

Thomassen, M. K., Sjøbakk, B., & Alfnes, E. (2014). A strategic approach for automation technology initiatives selection. In IFIP International Conference on Advances in Production Management Systems (pp. 288-295). Heidelberg: Springer.

Türkeș, M. C., Oncioiu, I., Aslam, H. D., Marin-Pantelescu, A., Topor, D. I., & Căpușneanu, S. (2019). Drivers and barriers in using Industry 4.0: a perspective of SMEs in Romania. Processes, 7(3), 153.

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.

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