Revisión de la Industria 4.0 y la toma de decisiones: Integración tecnológica y adaptación organizacional

Autores/as

DOI:

https://doi.org/10.61799/2216-0388.2368

Palabras clave:

Análisis bibliométrico, Industria 4.0, Sostenibilidad, Tecnologías, Toma de decisiones

Resumen

La acelerada transformación digital de las organizaciones ha consolidado a la Industria 4.0 como un enfoque clave que permite, a partir de la incorporación de diferentes tecnologías y la automatización de procesos, mejorar la eficiencia, la innovación, la competitividad y la toma de decisiones. Este artículo presenta los resultados de una revisión sistemática de la literatura sobre la Industria 4.0 y la toma de decisiones. Aprovechando herramientas como Bibliometrix, Tree of Science y Gephi, junto con los criterios de la declaración PRISMA 2020, se analizaron 1.738 registros bibliográficos de Scopus y Web of Science a partir de la construcción de un mapeo científico, la elaboración de un árbol temático y la identificación de los principales clústeres de investigación. Los resultados dan cuenta de que el campo de estudio se encuentra en una fase de madurez, con una marcada producción a cargo de China, India e Italia. Asimismo, se logró identificar una evolución temática que transita desde la conceptualización de la Industria 4.0 y la implementación de sus tecnologías hasta el desarrollo de soluciones tecnológicas y sistemas autónomos en entornos complejos. Finalmente, se presentan tres clústeres de investigación que giran alrededor de Industria 4.0 y Sistemas Ciberfísicos, patrones de implementación organizacional de la Industria 4.0 y la convergencia de tecnología y decisiones.

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Referencias

[1] D. Mourtzis, J. Angelopoulos, and N. Panopoulos, “A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0,” Energies, vol. 15, no. 17, p. 6276, Aug. 2022, doi: 10.3390/en15176276.

[2] A. Jamwal, R. Agrawal, M. Sharma, and A. Giallanza, “Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions,” Applied Sciences, vol. 11, no. 12, p. 5725, Jun. 2021, doi: 10.3390/app11125725.

[3] T. Zheng, M. Ardolino, A. Bacchetti, and M. Perona, “The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review,” International Journal of Production Research, pp. 1922–1954, Oct. 2020, doi: 10.1080/00207543.2020.1824085.

[4] A. E. H. Gabsi, “Integrating artificial intelligence in industry 4.0: insights, challenges, and future prospects–a literature review,” Annals of Operations Research, pp. 1–28, May 2024, doi: 10.1007/s10479-024-06012-6.

[5] A. Sartal, R. Bellas, A. M. Mejías, and A. García-Collado, “The sustainable manufacturing concept, evolution and opportunities within Industry 4.0: A literature review,” Advances in Mechanical Engineering, 2020, doi: 10.1177/1687814020925232.

[6] D. Ø. Madsen, K. Slåtten, and T. Berg, “From Industry 4.0 to Industry 6.0: Tracing the evolution of industrial paradigms through the lens of management fashion theory,” Systems, vol. 13, no. 5, p. 387, May 2025, doi: 10.3390/systems13050387.

[7] N. R. Haddaway, M. J. Page, C. C. Pritchard, and L. A. McGuinness, “PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis,” Campbell Systematic Reviews, vol. 18, no. 2, p. e1230, Jun. 2022, doi: 10.1002/cl2.1230.

[8] M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” Journal of Informetrics, vol. 11, no. 4, pp. 959–975, Nov. 2017, doi: 10.1016/j.joi.2017.08.007.

[9] J. D. Giraldo-Castellanos, P. M. Á. Alzate, and J. A. Arias-Bohórquez, “Prácticas de optimización de la cadena de suministro: mapeo de literatura,” Publ. investig., vol. 18, no. 1, Apr. 2024, doi: 10.22490/25394088.7521.

[10] P. Alzate et al., “Investigación operativa en educación: eficiencia, decisiones y realidades socioeconómicas,” Revista Lasallista De Investigación, vol. 23, no. 1, pp. 292–313, 2026, doi: 10.22507/rli.v23n1a3551.

[11] T. J. Parrado-Velásquez, G. A. Moyano-Londoño, and J. A. Vieira-Salazar, “Economía circular y cadenas de suministro: Mapeo científico, árbol de la ciencia y perspectivas de investigación,” Rev. fac. cien. econ., vol. 34, no. 1, May 2026, doi: 10.18359/rfce.8036.

[12] S. Robledo, G. Osorio, C. López, and Others, “Networking en pequeña empresa: una revisión bibliográfica utilizando la teoria de grafos,” Rev. Ordem Med., vol. 11, no. 2, pp. 6–16, 2014, [Online]. Available: https://revistas.udistrital.edu.co/index.php/vinculos/article/view/9664

[13] M. Bastian, S. Heymann, and M. Jacomy, “Gephi: An Open Source Software for Exploring and Manipulating Networks,” ICWSM, vol. 3, no. 1, pp. 361–362, Mar. 2009, doi: 10.1609/icwsm.v3i1.13937.

[14] K. W. Lim and W. Buntine, “Bibliographic analysis with the citation network topic model,” arXiv [cs.DL], Sep. 22, 2016. [Online]. Available: http://arxiv.org/abs/1609.06826

[15] C. Mejia, M. Wu, Y. Zhang, and Y. Kajikawa, “Exploring topics in bibliometric research through citation networks and semantic analysis,” Front. Res. Metr. Anal., vol. 6, p. 742311, Sep. 2021, doi: 10.3389/frma.2021.742311.

[16] G. A. Moyano-Londoño, J. E. Hernández, and M. A. Pava-Idárraga, “Propensity Score Matching and Marketing: A Scientometric Study, Tree of Science Analysis, and Research Perspectives,” Rev. CEA, vol. 12, no. 29, pp. e3479–e3479, May 2026, doi: 10.22430/24223182.3479.

[17] H. Lasi, P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann, “Industry 4.0,” Business & Information Systems Engineering, vol. 6, no. 4, pp. 239–242, Jun. 2014, doi: 10.1007/s12599-014-0334-4.

[18] A. G. Frank, L. S. Dalenogare, and N. F. Ayala, “Industry 4.0 technologies: Implementation patterns in manufacturing companies,” International Journal of Production Economics, vol. 210, pp. 15–26, Apr. 2019, doi: 10.1016/j.ijpe.2019.01.004.

[19] L. S. Dalenogare, G. B. Benitez, N. F. Ayala, and A. G. Frank, “The expected contribution of Industry 4.0 technologies for industrial performance,” Int. J. Prod. Econ., vol. 204, pp. 383–394, Oct. 2018, doi: 10.1016/j.ijpe.2018.08.019.

[20] F. Tao, Q. Qi, A. Liu, and A. Kusiak, “Data-driven smart manufacturing,” J. Manuf. Syst., vol. 48, pp. 157–169, Jul. 2018, doi: 10.1016/j.jmsy.2018.01.006.

[21] F. Tao, Q. Qi, L. Wang, and A. Y. C. Nee, “Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison,” Engineering (Beijing), vol. 5, no. 4, pp. 653–661, Aug. 2019, doi: 10.1016/j.eng.2019.01.014.

[22] L. D. Xu, E. L. Xu, and L. Li, “Industry 4.0: state of the art and future trends,” Int. J. Prod. Res., vol. 56, no. 8, pp. 2941–2962, Apr. 2018, doi: 10.1080/00207543.2018.1444806.

[23] S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, “Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives,” Process Saf. Environ. Prot., vol. 117, pp. 408–425, Jul. 2018, doi: 10.1016/j.psep.2018.05.009.

[24] M. Ghobakhloo, “Industry 4.0, digitization, and opportunities for sustainability,” J. Clean. Prod., vol. 252, no. 119869, p. 119869, Apr. 2020, doi: 10.1016/j.jclepro.2019.119869.

[25] K. Govindan, “How digitalization transforms the traditional circular economy to a smart circular economy for achieving SDGs and net zero,” Transp. Res. Part E: Logist. Trans. Rev., vol. 177, no. 103147, p. 103147, Sep. 2023, doi: 10.1016/j.tre.2023.103147.

[26] K. Govindan, “Unlocking the potential of quality as a core marketing strategy in remanufactured circular products: A machine learning enabled multi-theoretical perspective,” Int. J. Prod. Econ., vol. 269, no. 109123, p. 109123, Mar. 2024, doi: 10.1016/j.ijpe.2023.109123.

[27] Y. Kayikci, N. Gozacan-Chase, A. Rejeb, and K. Mathiyazhagan, “Critical success factors for implementing blockchain‐based circular supply chain,” Bus. Strategy Environ., vol. 31, no. 7, pp. 3595–3615, Nov. 2022, doi: 10.1002/bse.3110.

[28] A. Jamwal, R. Agrawal, and M. Sharma, “A framework to overcome blockchain enabled sustainable manufacturing issues through circular economy and industry 4.0 measures,” Int. j. math. eng. manag. sci., vol. 7, no. 6, pp. 764–790, Dec. 2022, doi: 10.33889/ijmems.2022.7.6.050.

[29] A. Raj, G. Dwivedi, A. Sharma, A. B. Lopes de Sousa Jabbour, and S. Rajak, “Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective,” Int. J. Prod. Econ., vol. 224, no. 107546, p. 107546, Jun. 2020, doi: 10.1016/j.ijpe.2019.107546.

[30] A. Singh, V. Kumar, P. Verma, and J. Kandasamy, “Identification and severity assessment of challenges in the adoption of industry 4.0 in Indian construction industry,” Asia Pac. Manag. Rev., vol. 28, no. 3, pp. 299–315, Sep. 2023, doi: 10.1016/j.apmrv.2022.10.007.

[31] O. Rodríguez-Espíndola, S. Chowdhury, P. K. Dey, P. Albores, and A. Emrouznejad, “Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing,” Technol. Forecast. Soc. Change, vol. 178, no. 121562, p. 121562, May 2022, doi: 10.1016/j.techfore.2022.121562.

[32] D. A. Rossit, F. Tohmé, and M. Frutos, “Industry 4.0: Smart scheduling,” Int. J. Prod. Res., vol. 57, no. 12, pp. 3802–3813, Jun. 2019, doi: 10.1080/00207543.2018.1504248.

[33] M. Sharma, S. Joshi, and K. Govindan, “Overcoming barriers to implement digital technologies to achieve sustainable production and consumption in the food sector: A circular economy perspective,” Sustain. Prod. Consum., vol. 39, pp. 203–215, Jul. 2023, doi: 10.1016/j.spc.2023.04.002.

[34] A. Yadav et al., “Challenges of blockchain adoption for manufacturing supply chain to achieve sustainability: A case of rubber industry,” Heliyon, vol. 10, no. 20, p. e39448, Oct. 2024, doi: 10.1016/j.heliyon.2024.e39448.

[35] T. I. Rakgoale, S. Bag, and J. H. C. Pretorius, “Analysis of barriers to the successful implementation of digitalization in South Africa using the interpretive Structural Modelling approach,” Global Bus. Rev., May 2024, doi: 10.1177/09721509241246253.

[36] I. Ansari, M. Barati, M. Ghobakhloo, and M. Fathi, “Aligning digitalization readiness assessment with strategic roadmapping for Industry 4.0 implementation in conglomerate manufacturers,” Prod. Plan. Control, pp. 1–21, Apr. 2026, doi: 10.1080/09537287.2026.2637578.

[37] M. Lubaba, M. I. Hosen, M. S. Shakur, M. A. Rahman, and A. B. M. M. Bari, “An intuitionistic fuzzy approach to assessing the barriers to quality 4.0 adoption in the footwear manufacturing industry: Implications for sustainability in emerging economy,” J. Open Innov., vol. 11, no. 3, p. 100604, Sep. 2025, doi: 10.1016/j.joitmc.2025.100604.

[38] M. Akhtar, A. K. Singh, and Y. Kayikci, “Strategic prioritization of integrated barriers to industry 4.0 and circular supply chain: Evidence from the Indian manufacturing sector,” Circ. Econ. Sustain., vol. 6, no. 3, Jun. 2026, doi: 10.1007/s43615-026-00919-x.

[39] T. Kiatcharoenpol and P. Sirisawat, “Evaluating critical barriers to industry 4.0 adoption in the Thai automotive sector using an integrated fuzzy BWM-PROMETHEE II-DEMATEL framework,” IEEE Access, vol. 14, pp. 34096–34112, 2026, doi: 10.1109/access.2026.3669303.

[40] J. M. Rožanec et al., “Human-centric artificial intelligence architecture for industry 5.0 applications,” Int. J. Prod. Res., vol. 61, no. 20, pp. 6847–6872, Oct. 2023, doi: 10.1080/00207543.2022.2138611.

[41] W. Li, N. Tian, and L. Zhang, “The Age of generative AI model for fresh industrial AIGC services: A hybrid-Action Multi-agent DRL approach,” Future Internet, vol. 18, no. 3, p. 172, Mar. 2026, doi: 10.3390/fi18030172.

[42] S. E. A. El Ahmadi and L. El Abbadi, “A predictive self-healing model for optimizing production lines: Integrating AI and IoT for autonomous fault detection and correction,” in SMILE 2025, Basel Switzerland: MDPI, Jun. 2025, p. 6. doi: 10.3390/engproc2025097006.

[43] E. Megdadi, A. Mohamed, and K. Shaalan, “Machine learning-driven Best–Worst Method for predictive maintenance in Industry 4.0,” Automation, vol. 6, no. 4, p. 91, Dec. 2025, doi: 10.3390/automation6040091.

[44] Y. Lu, “Industry 4.0: A survey on technologies, applications and open research issues,” J. Ind. Inf. Integr., vol. 6, pp. 1–10, Jun. 2017, doi: 10.1016/j.jii.2017.04.005.

[45] W. W. H. Kagermann, J. Helbig, and W. Wahlster, “High-tech strategy 2020,” 2013.

[46] L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Comput. Netw., vol. 54, no. 15, pp. 2787–2805, Oct. 2010, doi: 10.1016/j.comnet.2010.05.010.

[47] A. Kusiak, “Smart manufacturing,” Int. J. Prod. Res., vol. 56, no. 1–2, pp. 508–517, Jan. 2018, doi: 10.1080/00207543.2017.1351644.

[48] R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, “Intelligent manufacturing in the context of industry 4.0: A review,” Engineering (Beijing), vol. 3, no. 5, pp. 616–630, Oct. 2017, doi: 10.1016/j.eng.2017.05.015.

[49] M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,” in Transdisciplinary Perspectives on Complex Systems, Cham: Springer International Publishing, 2017, pp. 85–113. doi: 10.1007/978-3-319-38756-7_4.

[50] W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in manufacturing: A categorical literature review and classification,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 1016–1022, 2018, doi: 10.1016/j.ifacol.2018.08.474.

[51] A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital twin: Enabling technologies, challenges and open research,” IEEE Access, vol. 8, pp. 108952–108971, 2020, doi: 10.1109/access.2020.2998358.

[52] X. Xu, Y. Lu, B. Vogel-Heuser, and L. Wang, “Industry 4.0 and Industry 5.0—Inception, conception and perception,” J. Manuf. Syst., vol. 61, pp. 530–535, Oct. 2021, doi: 10.1016/j.jmsy.2021.10.006.

[53] S. Nahavandi, “Industry 5.0—A human-centric solution,” Sustainability, vol. 11, no. 16, p. 4371, Aug. 2019, doi: 10.3390/su11164371.

[54] D. Horváth and R. Z. Szabó, “Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities?,” Technol. Forecast. Soc. Change, vol. 146, pp. 119–132, Sep. 2019, doi: 10.1016/j.techfore.2019.05.021.

[55] Y. Liao, F. Deschamps, E. de Freitas Rocha Loures, and L. F. P. Ramos, “Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal,” International Journal of Production Research, Jun. 2017, doi: 10.1080/00207543.2017.1308576.

[56] A. Schumacher, S. Erol, and W. Sihn, “A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises,” Procedia CIRP, vol. 52, pp. 161–166, 2016, doi: 10.1016/j.procir.2016.07.040.

[57] M. Ghobakhloo, “The future of manufacturing industry: a strategic roadmap toward Industry 4.0,” Journal of Manufacturing Technology Management, vol. 29, no. 6, pp. 910–936, Jun. 2018, doi: 10.1108/JMTM-02-2018-0057.

[58] S.-V. Buer, J. O. Strandhagen, and F. T. S. Chan, “The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda,” Int. J. Prod. Res., vol. 56, no. 8, pp. 2924–2940, Apr. 2018, doi: 10.1080/00207543.2018.1442945.

[59] A. Moeuf, R. Pellerin, S. Lamouri, S. Tamayo-Giraldo, and R. Barbaray, “The industrial management of SMEs in the era of Industry 4.0,” Int. J. Prod. Res., vol. 56, no. 3, pp. 1118–1136, Feb. 2018, doi: 10.1080/00207543.2017.1372647.

[60] D. Ivanov, A. Dolgui, A. Das, and B. Sokolov, “Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility,” in Handbook of Ripple Effects in the Supply Chain, in International Series in Operations Research & Management Science. , Cham: Springer International Publishing, 2019, pp. 309–332. doi: 10.1007/978-3-030-14302-2_15.

[61] J. M. Müller, O. Buliga, and K.-I. Voigt, “Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0,” Technol. Forecast. Soc. Change, vol. 132, pp. 2–17, Jul. 2018, doi: 10.1016/j.techfore.2017.12.019.

[62] G. L. Tortorella and D. Fettermann, “Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies,” Int. J. Prod. Res., vol. 56, no. 8, pp. 2975–2987, Apr. 2018, doi: 10.1080/00207543.2017.1391420.

[63] A. B. L. de Sousa Jabbour, C. J. C. Jabbour, C. Foropon, and M. Godinho Filho, “When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors,” Technol. Forecast. Soc. Change, vol. 132, pp. 18–25, Jul. 2018, doi: 10.1016/j.techfore.2018.01.017.

[64] T. Stock and G. Seliger, “Opportunities of Sustainable Manufacturing in Industry 4.0,” Procedia CIRP, vol. 40, pp. 536–541, Jan. 2016, doi: 10.1016/j.procir.2016.01.129.

[65] C. Bai, P. Dallasega, G. Orzes, and J. Sarkis, “Industry 4.0 technologies assessment: A sustainability perspective,” Int. J. Prod. Econ., vol. 229, no. 107776, p. 107776, Nov. 2020, doi: 10.1016/j.ijpe.2020.107776.

[66] D. Kiel, J. M. Müller, C. Arnold, and K.-I. Voigt, “Sustainable industrial value creation: Benefits and challenges of industry 4.0,” Int. J. Innov. Manag., vol. 21, no. 08, p. 1740015, Dec. 2017, doi: 10.1142/s1363919617400151.

[67] C. G. Machado, M. P. Winroth, and E. H. D. Ribeiro da Silva, “Sustainable manufacturing in Industry 4.0: an emerging research agenda,” Int. J. Prod. Res., vol. 58, no. 5, pp. 1462–1484, Mar. 2020, doi: 10.1080/00207543.2019.1652777.

[68] E. Oztemel and S. Gursev, “Literature review of Industry 4.0 and related technologies,” J. Intell. Manuf., vol. 31, no. 1, pp. 127–182, Jan. 2020, doi: 10.1007/s10845-018-1433-8.

[69] S. Vaidya, P. Ambad, and S. Bhosle, “Industry 4.0 – A glimpse,” Procedia Manuf., vol. 20, pp. 233–238, 2018, doi: 10.1016/j.promfg.2018.02.034.

[70] S. H. Bonilla, H. R. O. Silva, M. Terra da Silva, R. Franco Gonçalves, and J. B. Sacomano, “Industry 4.0 and sustainability implications: A scenario-based analysis of the impacts and challenges,” Sustainability, vol. 10, no. 10, p. 3740, Oct. 2018, doi: 10.3390/su10103740.

[71] S. Luthra and S. K. Mangla, “Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies,” Process Saf. Environ. Prot., vol. 117, pp. 168–179, Jul. 2018, doi: 10.1016/j.psep.2018.04.018.

[72] G. Yadav, S. Luthra, S. K. Jakhar, S. K. Mangla, and D. P. Rai, “A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case,” J. Clean. Prod., vol. 254, no. 120112, p. 120112, May 2020, doi: 10.1016/j.jclepro.2020.120112.

[73] L. A. Zadeh, “Fuzzy sets,” Inf. Contr., vol. 8, no. 3, pp. 338–353, Jun. 1965, doi: 10.1016/s0019-9958(65)90241-x.

[74] J. Rezaei, “Best-worst multi-criteria decision-making method,” Omega, vol. 53, pp. 49–57, Jun. 2015, doi: 10.1016/j.omega.2014.11.009.

[75] F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven product design, manufacturing and service with big data,” Int. J. Adv. Manuf. Technol., vol. 94, no. 9–12, pp. 3563–3576, Feb. 2018, doi: 10.1007/s00170-017-0233-1.

Publicado

2026-09-01

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Artículos de Revisión

Cómo citar

[1]
Parrado Velasquez, T.J. et al. 2026. Revisión de la Industria 4.0 y la toma de decisiones: Integración tecnológica y adaptación organizacional. Mundo FESC. 16, 36 (Sep. 2026). DOI:https://doi.org/10.61799/2216-0388.2368.