A Study on the Success Cases about AI RPA (Robotic Process Automation) in Manufacturing Industry
AUTHORS
JeongBeom Kim,Head Professor of BigdataAI Dept., Namseoul University, Daehak Ro, Sunghwaneup, Choongnam, Korea
ABSTRACT
The main purpose of this paper is to study success cases about AI RPA (Robotic Process Automation) focus on the manufacturing industry. By implementing robotic process automation (RPA), an artificial intelligence solution at the manufacturing site, differentiation strategies such as maintenance of manufacturing systems, process innovation and efficiency, quality improvement, global collaboration system, and simulation of manufacturing business can be realized. Recently, the introduction of artificial intelligence technology that can reduce the manufacturing cost by building BPA around advanced manufacturing companies has been increasing the success of winter innovation. BPA technology is making use of PLM (Product Lifecycle Management) data at manufacturing sites. Data of PLM System, a framework of manufacturing sites, has integrated data that manages all phases of a product’s entire life cycle, from development ideas to disposal, so that the product’s lifecycle is based on three dimensions. In addition, the design is digitally processed, the prototypes are digitally verified, the production processes and production methods are digitally verified, and monitoring and simulation are supported. We will study how to differentiate the competitiveness of manufacturers through the case of building BPA, an artificial intelligence technology, in the manufacturing field.
KEYWORDS
AI, RPA, PLM, Manufacturing industry, Success case
REFERENCES
[1] KIAT, “Research Trend of Smart Manufacturing in U.S.A,” KIAT Industrial Technology Policy Brief, 2014-21, (2014)
[2] Gridone Ltd., “News Letter,” (2019)
[3] C. D. Scott and R. E. Smalley, “Diagnostic ultrasound: Principles and instruments,” Journal of Nanosci. Nanotechnology., vol.3, no.2, pp.75-80, (2003)
[4] Smith, T.F. and Waterman, M.S., “Identification of common molecular subsequences,”. J. Mol. Biol., vol.147, pp.195-197, (1981)
[5] Michael Negnevitsky, “Artificial intelligence,” Addison Welsley, (2017)
[6] H. S. Nalwa and Editor, “Magnetic nanostructures,” American Scientific Publishers, Los Angeles, (2003)
[7] Big Data Computing Technology, Hanbit Academy, pp.14-31, (2016)
[8] IBM project management report, New York, (2004)
[9] H. V. Jansen, N. R. Tas, and J. W. Berenschot, “Encyclopedia of nanoscience and nanotechnology,” Edited H. S. Nalwa, American Scientific Publishers, Los Angeles, vol.5, pp.163-275, (2004)
[10] Big Data Computing Technology, Hanbit Academy, pp.14-31, (2016)
[11] OkGi Kim, “Data science,” Ezies Publishing Co, pp.52-55, (2017)
[12] J. Kimura and H. Shibasaki, “Recent advances in clinical neurophysiology,” Proceedings of the 10th International Congress of EMG and Clinical Neurophysiology, Kyoto, Japan, October 15-19, (1995)
[13] World Economic Forum 1, “Big data, big impact: New possibilities for international development,” VITAL WAVE CONSULTING, (2012)
[14] H. V. Jansen, N. R. Tas, and J. W. Berenschot, “Encyclopedia of Nanoscience and Nanotechnology”, Edited H. S. Nalwa, American Scientific Publishers, Los Angeles, vol.5, pp.163-275, (2004)
[15] OkGi Kim, Data Science, Ezies Publishing Co, pp.52-55, (2017)
[16] D. Allen, “When axioms collide: An unfulfilled opportunity to advance knowledge for man and machine through automated reasoning”, International Conference on Computer Systems and Communication Technology, Shenzhen, China, (In Press. Proceedings to be published by Springer), Aug.7-9, (2016)
[17] Landauer T. K., Foltz P. W., and Laham D., “Introduction to Latent Semantic Analysis”, Discourse Processes, Vol.25, pp.259-284, (1998)