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The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.
Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.
The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.
The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.
The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.
A. Di Vaio, R. Palladino, R. Hassan, and O. Escobar. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research 121 283–314 (2020).
J. Ganzarain, and N. Errasti. Three stage maturity model in SME’s toward industry 4.0, Journal of Industrial Engineering and Management (JIEM), ISSN 2013-0953, OmniaScience, Barcelona, Vol. 9, Iss. 5, pp. 1119-1128 (2016).
I. Merediz-Solà and A. F. Bariviera. A bibliometric analysis of bitcoin scientific production. Research in International Business and Finance 50. 294–305. (2019)
P. K. Muhuri, K. Shukla, Amit, and A. Abraham. Industry 4.0. A bibliometric analysis and detailed overview. Eng. Appl. Artificial Intelligence 78, 218–235. (2019).
J. Van Eck and L. Waltman. (2020). Manual for VOSviewer version 1.6.15. Universiteit Leinden.
F. Walas Mateo and A. Redchuk (2021). The Emergence of New Business and Operating Models under the Industrial Digital Paradigm. Industrial Internet of Things, Platforms, and Artificial Intelligence/Machine Learning. Journal of Mechanics Engineering and Automation (JMEA) 11 (2), 54-60
G.D.N. Silveira, R.F. Viana, M.J. Lima, H.C. Kuhn, C.D.P. Crovato, S. B. Ferreira, G. Pesenti, E. Storck, and R. da Rosa Righ. I4.0 pilot project on a semiconductor industry: Implementation and lessons learned. Sensors 2020, (2020).
Yang, S., Navarathna, P., Ghosh, S., Bequette, B.W. Hybrid Modeling in the Era of Smart Manufacturing. Computers and Chemical Engineering. (2020).
P. Lara, M. Sánchez and J. Villalobos. Enterprise modelling and operational technologies (OT) application in the oil and gas industry. Journal of Industrial Information Integration. (2020).
E.B. Hansen and S. Bøgh. Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems. (2020).
Y. Park, J. Woo and S. Choi. A Cloud-based Digital Twin Manufacturing System based on an Interoperable Data Schema for Smart Manufacturing. International Journal of Computer Integrated Manufacturing. (2020).
Z. Merkaš, D. Perkov and V. Bonin. The significance of blockchain technology in digital transformation of logistics and transportation. International Journal of E-Services and Mobile Applications. (2020).
A. Seetharaman, N. Patwa, A.S. Saravanan and A. Sharma. Customer expectation from Industrial Internet of Things (IIOT). Journal of Manufacturing Technology Management. (2019).
J. Vater, L. Harscheidt and A. Knoll. Smart Manufacturing with Prescriptive Analytics. Proceedings of 2019 8th International Conference on Industrial Technology and Management, ICITM. (2019).
M. Khakifirooz, C.F. Chien and Y.J. Chen. Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0. Applied Soft Computing Journal. (2018).
Y. Wang, P. Zeng, H. Yu, Y. Zhang and X., Wang, Energy tree dynamics of smart grid based on industrial internet of things. Hindawi Publishing Corporation. International Journal of Distributed Sensor Networks. Volume 2013, Article ID 583846, 27 pages
Copyright (c) 2021 Federico Walas, Andrés Redchuk
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles accepted for publication will be licensed under the Creative Commons BY-NC-SA. Authors must sign a non-exclusive distribution agreement after article acceptance.
ISSN
1666-6038 (Online)
1666-6046 (Print)
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