The Role of Deep Learning in the Fourth Industrial Revolution: A Digital Transformation Approach
Keywords:
Digital Transformation, Deep Learning, Industry 4.0, Industrial Internet of Things (IIoT), Operations ManagementAbstract
Every day, new problems and shifts in the surrounding environment confront the industrial sector, which also has limited resources that need to be utilized to their fullest potential to do more with fewer resources. The advent of novel technologies underpinned by digitalization paves the way for many options and optimization opportunities that have never before been encountered in this industry. These developments are being referred to as "Industry 4.0." To achieve this goal, gathering, understanding, and making intelligent use of the massive amount of data generated in the industrial business environment is vital. This includes data regarding invoicing, manufacturing, procurement, the human factor, energy supplies, and a long etcetera. According to the study's findings, Industry 4.0 uses the Industrial Internet of Things (IIoT) to connect and flow data in digitizing factories. Additionally, it is possible to use management and control platforms that incorporate Machine Learning to optimize processes based on Artificial Intelligence learning. These findings are summarized. On the other hand, on this road leading to the future, a new strategy has evolved that goes one step further, deep learning.
Downloads
References
Gerlitz, L. (2016). Design management as a domain of smart and sustainable enterprise: business modelling for innovation and smart growth in Industry 4.0. Entrepreneurship and Sustainability Issues, 3(3), 244-268. https://doi.org/10.9770/jesi.2016.3.3(3)
Guersent, O. (2016). LA TRANSFORMACIÓN DIGITAL DE LOS INSTRUMENTOS DE PAGO. [[THE DIGITAL TRANSFORMATION OF PAYMENT INSTRUMENTS]] Papeles De Economía Española, (149), 58-61,172.
Gutlapalli, S. S. (2016). An Examination of Nanotechnology’s Role as an Integral Part of Electronics. ABC Research Alert, 4(3), 21–27. https://doi.org/10.18034/ra.v4i3.651
Mandapuram, M. (2016). Applications of Blockchain and Distributed Ledger Technology (DLT) in Commercial Settings. Asian Accounting and Auditing Advancement, 7(1), 50–57. Retrieved from https://4ajournal.com/article/view/76
Matt, C., Hess, T., & Benlian, A. (2015). Digital Transformation Strategies. Business & Information Systems Engineering, 57(5), 339-343. https://doi.org/10.1007/s12599-015-0401-5
Mayhew, M. J., Seifert, T. A., Pascarella, E. T., Nelson Laird, T.,F., & Blaich, C. F. (2012). Going Deep into Mechanisms for Moral Reasoning Growth: How Deep Learning Approaches Affect Moral Reasoning Development for First-year Students. Research in Higher Education, 53(1), 26-46. https://doi.org/10.1007/s11162-011-9226-3
O'Leary, J. (2014). Better Public Services and digital transformation. Public Sector, 37(4), 14-16.
Prause, G. (2016). E-Residency: a business platform for Industry 4.0? Entrepreneurship and Sustainability Issues, 3(3), 216-227. https://doi.org/10.9770/jesi.2016.3.3(1)
Tamás, P., Illés, B., & Dobos, P. (2016). Waste reduction possibilities for manufacturing systems in the industry 4.0. IOP Conference Series.Materials Science and Engineering, 161(1). https://doi.org/10.1088/1757-899X/161/1/012074
Westerman, G., Bonnet, D., & McAfee, A. (2014). The Nine Elements of Digital Transformation. MIT Sloan Management Review, 55(3), 1-6.
Wilhelm, J. D. (2014). Learning to love the questions: HOW ESSENTIAL QUESTIONS PROMOTE CREATIVITY AND DEEP LEARNING. Knowledge Quest, 42(5), 36-41.
Zhang, X., Peek, W. A., Pikas, B., & Lee, T. (2016). The Transformation and Upgrading of the Chinese Manufacturing Industry: Based on "German Industry 4.0". The Journal of Applied Business and Economics, 18(5), 97-105.
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Asian Accounting and Auditing Advancement
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
